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High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them. [ABSTRACT FROM AUTHOR]

Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specifically, a deformable cross-scale Transformer module is devised, incorporating a multi-scale offset mechanism that non-uniformly samples features at different scales, enhancing the spatial information aggregation capability of the output features. Simultaneously, to address the issue of feature misalignment during multi-frame feature fusion, a deformable cross-frame Transformer module is proposed. This module incorporates independently learnable offset parameters for different frame features, enabling the model to adaptively correlate dynamic features across multiple frames and improve the temporal information utilization of the model. A proposal-aware sampling algorithm is introduced to significantly increase the foreground point recall, further optimizing the efficiency of feature extraction. The obtained multi-scale and multi-frame voxel features are subjected to an adaptive fusion weight extraction module, referred to as the proposed mixed voxel set extraction module. This module allows the model to adaptively obtain mixed features containing both spatial and temporal information. The effectiveness of the proposed algorithm is validated on the KITTI, nuScenes, and self-collected urban datasets. The proposed algorithm achieves an average precision improvement of 2.1% over the latest multi-frame-based algorithms. [ABSTRACT FROM AUTHOR]

Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a "teacher-assistant" model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the "teacher-assistant" model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are currently one of the most intensively developing branches of marine technology. Their widespread use and versatility allow them to perform tasks that, until recently, required human resources. One problem in AUVs is inadequate navigation, which results in inaccurate positioning. Weaknesses in electronic equipment lead to errors in determining a vehicle's position during underwater missions, requiring periodic reduction of accumulated errors through the use of radio navigation systems (e.g., GNSS). However, these signals may be unavailable or deliberately distorted. Therefore, in this paper, we propose a new computer vision-based method for estimating the position of an AUV. Our method uses computer vision and deep learning techniques to generate the surroundings of the vehicle during temporary surfacing at the point where it is currently located. The next step is to compare this with the shoreline representation on the map, which is generated for a set of points that are in a specific vicinity of a point determined by dead reckoning. This method is primarily intended for low-cost vehicles without advanced navigation systems. Our results suggest that the proposed solution reduces the error in vehicle positioning to 30–60 m and can be used in incomplete shoreline representations. Further research will focus on the use of the proposed method in fully autonomous navigation systems. [ABSTRACT FROM AUTHOR]

Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle's bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle's detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles. [ABSTRACT FROM AUTHOR]

LiDAR-based Multi-Object Tracking (MOT) is a critical technology employed in various autonomous systems, including self-driving vehicles and autonomous delivery robots. In this paper, a novel LiDAR-based 3D MOT approach is introduced. The proposed method was built upon the Tracking-by-Detection (TbD) paradigm and incorporated multi-level associations that exploit an object's short-term and long-term relationships with the existing tracks. Specifically, the short-term association leverages the fact that objects do not move much between consecutive frames. In contrast, the long-term association assesses the degree to which a long-term trajectory aligns with current detections. The evaluation of the matching between the current detection and the maintained trajectory was performed using a Graph Convolutional Network (GCN). Furthermore, an inactive track was maintained to address the issue of incorrect ID switching for objects that have been occluded for an extended period. The proposed method was evaluated on the KITTI benchmark MOT tracking dataset and achieved a Higher-Order Tracking Accuracy (HOTA) of 75.65%, marking a 5.66% improvement over the benchmark method AB3DMOT, while also accomplishing the number of ID switches of 39, 74 less than AB3DMOT. These results confirmed the effectiveness of the proposed approach in diverse road environments. [ABSTRACT FROM AUTHOR]

LiDAR odometry is a fundamental task for high-precision map construction and real-time and accurate localization in autonomous driving. However, point clouds in urban road scenes acquired by vehicle-borne lasers are of large amounts, "near dense and far sparse" density, and contain different dynamic objects, leading to low efficiency and low accuracy of existing LiDAR odometry methods. To address the above issues, a simulation-based self-supervised line extraction in urban road scene is proposed, as a pre-processing for LiDAR odometry to reduce the amount of input and the interference from dynamic objects. A simulated dataset is first constructed according to the characteristics of point clouds in urban road scenes; and then, an EdgeConv-based network, named LO-LineNet, is used for pre-training; finally, a model transferring strategy is adopted to transfer the pre-trained model from a simulated dataset to real-world scenes without ground-truth labels. Experimental results on the KITTI Odometry Dataset and the Apollo SouthBay Dataset indicate that the proposed method can accurately extract reliable lines in urban road scenes in a self-supervised way, and the use of the extracted reliable lines as input for odometry can significantly improve its accuracy and efficiency in urban road scenes. [ABSTRACT FROM AUTHOR]

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale features of radar point clouds and images, followed by upsampling and fusion of the feature maps. Then, we leverage a multi-layer perceptron to nonlinearly represent the fused features, reducing the feature dimensionality to improve model inference speed. Within the context cluster block, we aggregate feature points of the same object from different sensors into one cluster based on their similarity. All feature points within the same cluster are then fused into a radar–camera feature fusion point, which is self-adaptively reassigned to the originally extracted feature points from a simplex sensor. Compared to previous methods that only utilize radar as an auxiliary sensor to camera, or vice versa, the ConCs-Fusion method achieves a bidirectional cross-modal fusion between radar and camera. Finally, our extensive experiments on the nuScenes dataset demonstrate that ConCs-Fusion outperforms other methods in terms of 3D object detection performance. [ABSTRACT FROM AUTHOR]

Real-time Kinematic (RTK) positioning provides centimeter-level positioning accuracy within several seconds, but it has to rely on a nearby base station. Although Precise Point Positioning (PPP) supplies precise positions with one receiver, its convergence time takes several tens of minutes, which makes PPP not well suited for real-time kinematic applications where a rapid convergence is required. PPP-RTK integrates the benefits of PPP and RTK, which actually is PPP augmented by a ground GNSS network. The satellite orbit, clock offsets, signal biases, ionospheric and tropospheric corrections are determined based on this GNSS network, modeled as state space information and transmitted to PPP users. By applying these State Space Representation (SSR) corrections, a real-time kinematic PPP-RTK approach is developed and implemented, which can instantly resolve the ambiguities to integers and realize rapid convergence. In a static scenario, it realized an instant ambiguity resolution and a rapid convergence within 2 s in more than 90% of 120 hourly sessions. The PPP-RTK has been applied and evaluated in a kinematic scenario on the highway. The horizontal positioning errors are almost lower than 0.1 m except for the time of passing through bridges. After passing bridges, the PPP-RTK successfully resolved ambiguities within 2 s in 90.6% of the cases and achieved convergence in horizontal within 5 s in more than 90% of the cases. The PPP-RTK with a precision of 0.1 m and rapid convergence of several seconds benefits the precise navigation of automobile on the highway, which will support the development of autonomous driving in future. [ABSTRACT FROM AUTHOR]

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods. [ABSTRACT FROM AUTHOR]

Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data's position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collected 3D points is low, and its distribution is unbalanced in the scene, which influences the accuracy of 3D object detectors in regards to object location and identification. Although corresponding high-resolution scene images from cameras can be used as supplemental information, poor fusion strategies can result in decreased accuracy compared with that of LiDAR-point-only detectors. Thus, to improve the detection performance for the classification, localization, and even boundary location of 3D objects, a two-stage detector with density-and-sparsity feature aggregation, called DASANet, is proposed in this paper. In the first stage, dense pseudo point clouds are generated with images from cameras and are used to obtain the initial proposals. In the second stage, two novel feature aggregation modules are designed to fuse LiDAR point information and pseudo point information, which refines the semantic and detailed representation of the feature maps. To supplement the semantic information of the highest-scale LiDAR features for object localization and classification, a triple differential information supplement (TDIS) module is presented to extract the LiDAR-pseudo differential features and enhance them in spatial, channel, and global dimensions. To increase the detailed information of the LiDAR features for object boundary location, a Siamese three-dimension coordinate attention (STCA) module is presented to extract stable LiDAR and pseudo point cloud features with a Siamese encoder and fuse these features using a three-dimension coordinate attention. Experiments using the KITTI Vision Benchmark Suite demonstrate the improved performance of our DASANet in regards to the localization and boundary location of objects. The ablation studies demonstrate the effectiveness of the TDIS and the STCA modules. [ABSTRACT FROM AUTHOR]

UAVs represent a tremendous opportunity to perform geophysical and repeated experiments, particularly in volcanic contexts. Their ability to be deployed rapidly and fly at various altitudes and the fact that they are easy to operate despite complex field conditions make them attractive for magnetic surveys. Detailed maps of the magnetic field in turn bring key constraints on the rocks' composition, thermal anomalies, intrusive systems, and crustal contrast evolution. Yet, raw magnetic field measurements require careful processing to minimize directional, positional, and crossover errors. Moreover, stitching together adjacent or overlapping surveys acquired at different times and altitudes is not a trivial task. Therefore, it is challenging in remote areas to directly evaluate the consistency of a survey and to ascertain the success of the field mission. In this paper, we present a fast algorithm allowing for a quick-look modeling of scalar magnetic intensity measurements. The approach relies on rectangular harmonic analysis (RHA). The field measurements are automatically corrected for a global main field. Then, they are projected along this main field and modeled in terms of RHA functions. The software can exploit the quality indices provided with data and a procedure is applied to mitigate the effect of outliers. Maps for the scalar and the vector anomaly fields are readily built on an interpolated regular grid leveled at a constant altitude. In order to assess the modeling and the inversion procedures, analyses are carried out with synthetic measurements derived from a high-resolution global lithospheric magnetic field model estimated on the French aeromagnetic grid and at UAV locations with some added nonrandom noise. These analyses indicate that RHA is efficient for first-order and direct mapping of the crustal magnetic field structures measured by UAVs but that it could be applied on airborne and marine magnetic intensity data covering dense and large geographical extensions. [ABSTRACT FROM AUTHOR]

Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. In this article, we focus on achieving accurate 2D object detection in urban autonomous driving scenarios. Considering the occlusion issues of using a single sensor from a single viewpoint, as well as the limitations of current vision-based approaches in bad weather conditions, we propose a novel multi-modal sensor fusion network called LRVFNet. This network effectively combines data from LiDAR, mmWave radar, and visual sensors through a deep multi-scale attention-based architecture. LRVFNet comprises three modules: a backbone responsible for generating distinct features from various sensor modalities, a feature fusion module utilizing the attention mechanism to fuse multi-modal features, and a pyramid module for object reasoning at different scales. By effectively fusing complementary information from multi-modal sensory data, LRVFNet enhances accuracy and robustness in 2D object detection. Extensive evaluations have been conducted on the public VOD dataset and the Flow dataset. The experimental results demonstrate the superior performance of our proposed LRVFNet compared to state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. [ABSTRACT FROM AUTHOR]

Accurate and robust localization using multi-modal sensors is crucial for autonomous driving applications. Although wheel encoder measurements can provide additional velocity information for visual-inertial odometry (VIO), the existing visual-inertial-wheel odometry (VIWO) still cannot avoid long-term drift caused by the low-precision attitude acquired by the gyroscope of a low-cost inertial measurement unit (IMU), especially in visually restricted scenes where the visual information cannot accurately correct for the IMU bias. In this work, leveraging the powerful data processing capability of deep learning, we propose a novel tightly coupled monocular visual-inertial-wheel odometry with neural gyroscope calibration (NGC) to obtain accurate, robust, and long-term localization for autonomous vehicles. First, to cure the drift of the gyroscope, we design a robust neural gyroscope calibration network for low-cost IMU gyroscope measurements (called NGC-Net). Following a carefully deduced mathematical calibration model, NGC-Net leverages the temporal convolutional network to extract different scale features from raw IMU measurements in the past and regress the gyroscope corrections to output the de-noised gyroscope. A series of experiments on public datasets show that our NGC-Net has better performance on gyroscope de-noising than learning methods and competes with state-of-the-art VIO methods. Moreover, based on the more accurate de-noised gyroscope, an effective strategy for combining the advantages of VIWO and NGC-Net outputs is proposed in a tightly coupled framework, which significantly improves the accuracy of the state-of-the-art VIO/VIWO methods. In long-term and large-scale urban environments, our RNGC-VIWO tracking system performs robustly, and experimental results demonstrate the superiority of our method in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. This paper proposes a novel 3D ArcSAR method to address this issue, which combines rotational synthetic aperture radar (SAR) and direction estimation algorithms. The method aims to reconstruct 3D images of 360° scenes and offers distinctive advantages in both azimuthal and altitudinal sensing. Nevertheless, due to the unique structural characteristics of vehicle SAR, it is limited to receiving only a single snapshot signal for 3D sensing. We propose a resolution algorithm based on ArcSAR and the iterative adaptive approach (IAA) to resolve the limitation. Furthermore, the errors in altitude angle estimation of the proposed algorithm and conventional algorithms are analyzed under various conditions, including different target spacing and signal-to-noise ratio (SNR). Finally, we design and implement a prototype of the 3D ArcSAR sensing system, which utilizes a millimeter-wave MIMO radar system and a rotating scanning mechanical system. The experimental results obtained from this prototype effectively validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes. [ABSTRACT FROM AUTHOR]

The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: point cloud clustering, bounding box fitting and point cloud recognition. Firstly, aiming at the problem of clustering quality degradation caused by the uneven distribution of LiDAR point clouds and the difference in clustering radius between point cloud clusters in traditional DBSCAN (TDBSCAN) obstacle clustering algorithms, an improved DBSCAN algorithm based on distance-adaptive clustering radius (ADBSCAN) is designed, and a point cloud KD-Tree data structure is constructed to speed up the traversal of the algorithm; meanwhile, the OPTICS algorithm is introduced to enhance the performance of the proposed algorithm. Then, by adopting different fitting strategies for vehicle contour points in various states, the adaptability of the bounding box fitting algorithm is improved; Moreover, in view of the shortcomings of the poor robustness of the L-shape algorithm, the principal component analysis method (PCA) is introduced to obtain stable bounding box fitting results. Finally, considering the time-consuming and low-accuracy training of traditional machine learning algorithms, advanced PointNet in deep learning technique is built to send the point cloud within the bounding box of a high-confidence vehicle into PointNet to complete vehicle recognition. Experiments based on our autonomous driving perception platform and the KITTI dataset prove that the proposed method can stably complete vehicle target recognition and achieve a good balance between time-consuming and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle's velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making. [ABSTRACT FROM AUTHOR]

An important research direction in the field of traffic light recognition of autonomous systems is to accurately obtain the region of interest (ROI) of the image through the multi-sensor assisted method. Dynamic evaluation of the performance of the multi-sensor (GNSS, IMU, and odometer) fusion positioning system to obtain the optimum size of the ROI is essential for further improvement of recognition accuracy. In this paper, we propose a dynamic estimation adjustment (DEA) model construction method to optimize the ROI. First, according to the residual variance of the integrated navigation system and the vehicle velocity, we divide the innovation into an approximate Gaussian fitting region (AGFR) and a Gaussian convergence region (GCR) and estimate them using variational Bayesian gated recurrent unit (VBGRU) networks and a Gaussian mixture model (GMM), respectively, to obtain the GNSS measurement uncertainty. Then, the relationship between the GNSS measurement uncertainty and the multi-sensor aided ROI acquisition error is deduced and analyzed in detail. Further, we build a dynamic estimation adjustment model to convert the innovation of the multi-sensor integrated navigation system into the optimal ROI size of the traffic lights online. Finally, we use the YOLOv4 model to detect and recognize the traffic lights in the ROI. Based on laboratory simulation and real road tests, we verify the performance of the DEA model. The experimental results show that the proposed algorithm is more suitable for the application of autonomous vehicles in complex urban road scenarios than the existing achievements. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult to maintain robust situation awareness in a variety of driving situations. To enhance AV perception, methods to integrate sensor data from the camera, radar, and LiDAR sensors have been proposed. However, due to rigidity in their fusion implementations, current techniques are not sufficiently robust in challenging driving scenarios (such as inclement weather, poor light, and sensor obstruction). These techniques can be divided into two main groups: (i) early fusion, which is ineffective when sensor data are distorted or noisy, and (ii) late fusion, which is unable to take advantage of characteristics from numerous sensors and hence yields sub-optimal estimates. In this paper, we suggest a flexible selective sensor fusion framework that learns to recognize the present driving environment and fuses the optimum sensor combinations to enhance robustness without sacrificing efficiency to overcome the above-mentioned limitations. The proposed framework dynamically simulates early fusion, late fusion, and mixtures of both, allowing for a quick decision on the best fusion approach. The framework includes versatile modules for pre-processing heterogeneous data such as numeric, alphanumeric, image, and audio data, selecting appropriate features, and efficiently fusing the selected features. Further, versatile object detection and classification models are proposed to detect and categorize objects accurately. Advanced ensembling, gating, and filtering techniques are introduced to select the optimal object detection and classification model. Further, innovative methodologies are proposed to create an accurate context and decision rules. Widely used datasets like KITTI, nuScenes, and RADIATE are used in experimental analysis to evaluate the proposed models. The proposed model performed well in both data-level and decision-level fusion activities and also outperformed other fusion models in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectures, but their interactive benefits are seldom taken into account. In this paper, we present a novel approach using cross-view knowledge generative techniques in combination with transformers, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, by taking the initial representations produced by the backbone network, MGTL develops two separate generative sub-modules—one for aerial-aware knowledge generation from ground-view semantics and vice versa—and fully exploits the entirely mutual benefits through the attention mechanism. Moreover, to better capture the co-visual relationships between aerial and ground views, we introduce a cascaded attention masking algorithm to further boost accuracy. Extensive experiments on challenging public benchmarks, i.e., CVACT and CVUSA, demonstrate the effectiveness of the proposed method, which sets new records compared with the existing state-of-the-art models. Our code will be available upon acceptance. [ABSTRACT FROM AUTHOR]

Autonomous vehicles contain many sensors, enabling them to drive by themselves. Autonomous vehicles need to communicate with other vehicles (V2V) wirelessly and with infrastructures (V2I) like satellites with diverse connections as well, to implement safety, reliability, and efficiency. Information transfer from remote communication appliances is a critical task and should be accomplished quickly, in real time and with maximum reliability. A message that arrives late, arrives with errors, or does not arrive at all can create an unsafe situation. This study aims at employing data compression to efficiently transmit GNSS information to an autonomous vehicle or other infrastructure such as a satellite with maximum accuracy and efficiency. We developed a method for compressing NMEA data. Furthermore, our results were better than other ones in current studies, while supporting error tolerance and data omission. [ABSTRACT FROM AUTHOR]

Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable appearance characteristics of pedestrians and the frequent occlusion interaction, which leads to the insufficient accuracy and stability of tracking. Therefore, this paper proposes a detector–tracker integration framework for autonomous vehicle pedestrian tracking. Firstly, a pedestrian objects detector based on the improved YOLOv7 network was established. Space-to-Depth convolution layer was adopted to improve the backbone network of YOLOv7. Then, a novel appearance feature extraction network is proposed, which integrates the convolutional structural re-parameterization idea to construct a full-scale feature extraction block, which is the optimized DeepSORT tracker. Finally, experiments were carried out on MOT17 and MOT20 public datasets and driving video sequences, and the tracking performance of the proposed framework was evaluated by comparing it with the most advanced multi-object tracking algorithms. Quantitative analysis results show that the framework has high tracking accuracy. Compared with DeepSORT, MOTA improves by 2.3% in the MOT17 dataset and MOTA improves by 4.2% in the MOT20 dataset. Through qualitative evaluation on real driving video sequences, the framework proposed in this paper is robust in a variety of climate environments, and can be effectively applied to the pedestrian tracking of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses, or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We proposed to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO, and the BSB-Aerial dataset. Our models outperformed the state-of-the-art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on an RTX3090 GPU with FP16 precision and TensorRT optimization. [ABSTRACT FROM AUTHOR]

Accurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds for 3D proposal refinement. However, the sparsity of point clouds, particularly for faraway points, makes it difficult for the LiDAR-only refinement module to recognize and locate objects accurately. To address this issue, we propose a novel multi-modality two-stage approach called FusionRCNN. This approach effectively and efficiently fuses point clouds and camera images in the Regions of Interest (RoI). The FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from the camera in a unified attention mechanism. Specifically, FusionRCNN first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step. Then, it leverages an intra-modality self-attention to enhance the domain-specific features, followed by a well-designed cross-attention to fuse the information from two modalities. FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors. Remarkably, FusionRCNN improves the strong SECOND baseline by 6.14% mAP on Waymo and outperforms competing two-stage approaches. [ABSTRACT FROM AUTHOR]

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points. [ABSTRACT FROM AUTHOR]

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods. [ABSTRACT FROM AUTHOR]

Autonomous driving is becoming a pivotal technology that can realize intelligent transportation and revolutionize the future of mobility. Various types of sensors, including perception sensors and localization sensors, are essential for high-level autonomous and intelligent vehicles (AIV). In this paper, the characteristics of different sensors are compared, and the application characteristics and requirements of AIV are analyzed in depth. These analyses indicate that: GNSS, as the unique localization sensor that can obtain an absolute position, can not only provide all-weather position and time information for internal multi-sensor fusion but also act as a standard spatiotemporal reference for all autonomous systems; Furthermore, AIVs aim to provide safety for a mass user base ranging from tens to hundreds of millions; for this, AIVs require a global wide-area and instantaneous precise positioning service with location privacy protection. Based on a "geometry-bound" description of road grade and vehicle size, it has been found that GNSS requirements in autonomous vehicles include decimeter-level positioning with the assurance of high integrity. Combined with high-integrity GNSS implementation in the civil aviation field, GNSS different technology routes, and commercial solutions, a state space representation (SSR)-based GNSS high-precision augmentation positioning solution for AIV is summarized and introduced. The solution can achieve instantaneous, precise positioning with high integrity in a wide area by utilizing passive positioning mode with location privacy protection. In addition, the research progress on key technologies in the solution and existing challenges is investigated in detail by reviewing a series of publications. [ABSTRACT FROM AUTHOR]

Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF, point-cloud multiple-pixel fusion, for 3D object detection. PMPF projects the point cloud data onto the image plane, where the region pixels are processed to correspond with the points and decorated to the point cloud data, such that the fused point cloud data can be applied to LiDAR-only detectors with autoencoders. PMPF is a plug-and-play, decoupled multi-sensor fusion detection framework with low latency. Extensive experiments on the KITTI 3D object detection benchmark show that PMPF vastly improves upon most of the LiDAR-only detectors, e.g., PointPillars, SECOND, CIA-SSD, SE-SSD four state-of-the-art one-stage detectors, and PointRCNN, PV-RCNN, Part-A2 three two-stage detectors. [ABSTRACT FROM AUTHOR]

Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoint-based 3D object detector with monocular vision, named Keypoint3D. We creatively leveraged 2D projected points from 3D objects' geometric centers as keypoints for object modeling. Additionally, for precise keypoints positioning, we utilized a novel self-adapting ellipse Gaussian filter (saEGF) on heatmaps, considering different objects' shapes. We tried different variations of DLA-34 backbone and proposed a semi-aggregation DLA-34 (SADLA-34) network, which pruned the redundant aggregation branch but achieved better performance. Keypoint3D regressed the yaw angle in a Euclidean space, which resulted in a closed mathematical space avoiding singularities. Numerous experiments on the KITTI dataset for a moderate level have proven that Keypoint3D achieved the best speed-accuracy trade-off with an average precision of 39.1 % at 18.9 FPS on 3D cars detection. [ABSTRACT FROM AUTHOR]

The map-based localization method is considered an effective supplement to the localization under the GNSS-denied environment. However, since the map is constituted by the dispersed keyframes, it sometimes happens that the initial position of the unmanned ground vehicle (UGV) lies between the map keyframes or is not on the mapping trajectory. In both cases, it will be impossible to precisely estimate the pose of the vehicle directly via the relationship between the current frame and the map keyframes, leading to localization failure. In this regard, we propose a localization algorithm based on the global descriptor and dynamic range search (LA-GDADRS). In specific, we first design a global descriptor shift and rotation invariant image (SRI), which improves the rotation invariance and shift invariance by the methods of coordinates removal and de-centralization. Secondly, we design a global localization algorithm for shift and rotation invariant branch-and-bound scan matching (SRI-BBS). It first leverages SRI to obtain an approximate priori position of the unmanned vehicle and then utilizes the similarity between the current frame SRI and the map keyframes SRI to select a dynamic search range around the priori position. Within the search range, we leverage the branch-and-bound scanning matching algorithm to search for a more precise pose. It solves the problem that global localization tends to fail when the priori position is imprecise. Moreover, we introduce a tightly coupled factor graph model and a HD map engine to achieve real-time position tracking and lane-level localization, respectively. Finally, we complete extensive ablation experiments and comparative experiments to validate our methods on the benchmark dataset (KITTI) and the real application scenarios at the campus. Extensive experimental results demonstrate that our algorithm achieves the performance of mainstream localization algorithms. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system. [ABSTRACT FROM AUTHOR]

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. However, there exist cases where sensors cannot be well-calibrated. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. The method can be directly used on the pictures taken in the field without complex preprocessing. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations. [ABSTRACT FROM AUTHOR]

LiDAR-based semantic segmentation, particularly for unstructured environments, plays a crucial role in environment perception and driving decisions for unmanned ground vehicles. Unfortunately, chaotic unstructured environments, especially the high-proportion drivable areas and large-area static obstacles therein, inevitably suffer from the problem of blurred class edges. Existing published works are prone to inaccurate edge segmentation and have difficulties dealing with the above challenge. To this end, this paper proposes a real-time edge-guided LiDAR semantic segmentation network for unstructured environments. First, the main branch is a lightweight architecture that extracts multi-level point cloud semantic features; Second, the edge segmentation module is designed to extract high-resolution edge features using cascaded edge attention blocks, and the accuracy of extracted edge features and the consistency between predicted edge and semantic segmentation results are ensured by additional supervision; Third, the edge guided fusion module fuses edge features and main branch features in a multi-scale manner and recalibrates the channel feature using channel attention, realizing the edge guidance to semantic segmentation and further improving the segmentation accuracy and adaptability of the model. Experimental results on the SemanticKITTI dataset, the Rellis-3D dataset, and on our test dataset demonstrate the effectiveness and real-time performance of the proposed network in different unstructured environments. Especially, the network has state-of-the-art performance in segmentation of drivable areas and large-area static obstacles in unstructured environments. [ABSTRACT FROM AUTHOR]

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) object tracking is critical in 3D computer vision. It has applications in autonomous driving, robotics, and human–computer interaction. However, methods for using multimodal information among objects to increase multi-object detection and tracking (MOT) accuracy remain a critical focus of research. Therefore, we present a multimodal MOT framework for autonomous driving boost correlation multi-object detection and tracking (BcMODT) in this research study to provide more trustworthy features and correlation scores for real-time detection tracking using both camera and LiDAR measurement data. Specifically, we propose an end-to-end deep neural network using 2D and 3D data for joint object detection and association. A new 3D mixed IoU (3D-MiIoU) computational module is also developed to acquire more precise geometric affinity by increasing the aspect ratio and length-to-height ratio between linked frames. Meanwhile, a boost correlation feature (BcF) module is proposed for the affinity calculation of the appearance of similar objects, which comprises an appearance affinity calculation module for similar objects in adjacent frames that are calculated directly using the feature distance and feature direction's similarity. The KITTI tracking benchmark shows that our method outperforms other methods with respect to tracking accuracy. [ABSTRACT FROM AUTHOR]

Climate change poses special and new challenges to inland waters, requiring intensive monitoring. An application based on an autonomous operation swimming vehicle (ASV) is being developed that will provide simulations, spatially and depth-resolved water parameter monitoring, bathymetry detection, and respiration measurement. A clustered load system is integrated with a high-resolution sonar system and compared with underwater photogrammetry objects. Additionally, a holistic 3D survey of the water body above and below the water surface is generated. The collected data are used for a simulation environment to train artificial intelligence (AI) in virtual reality (VR). These algorithms are used to improve the autonomous control of the ASV. In addition, possibilities of augmented reality (AR) can be used to visualize the data of the measurements and to use them for future ASV assistance systems. The results of the investigation into a flooded quarry are explained and discussed. There is a comprehensive, high-potential, simple, and rapid monitoring method for inland waters that is suitable for a wide range of scientific investigations and commercial uses due to climate change, simulation, monitoring, analyses, and work preparation. [ABSTRACT FROM AUTHOR]

High-precision and robust localization in GNSS-denied areas is crucial for autonomous vehicles and robots. Most state-of-the-art localization methods are based on simultaneous localization and mapping (SLAM) with a camera or light detection and ranging (LiDAR). However, SLAM will suffer from drift during long-term running without loop closure or prior constraints. Lightweight, high-precision environmental maps have gradually become an indispensable part of future autonomous driving. In order to solve the problem of real-time global localization for autonomous vehicles and robots, we propose a precise and robust LiDAR localization system based on a pre-built, occupied high-definition (HD) map called the Extended QuadTree (EQT) map. It makes use of a planar quadtree for block division and a Z-sequence index structure within the block cells. Then, a four-level occupancy probability cell value model is adopted. It will save about eight times the storage space compared with Google Cartographer, and the EQT map can be extended to store other information. For efficient scan-to-map matching with our specialized EQT map, the Bursa linearized model is used in the Gauss–Newton iteration of our algorithm, which makes the calculation of partial derivatives fast. All the above improvements lead to optimal storage and efficient querying for real-time scan-to-map matching localization. Field tests in an industrial park and road environment prove that positioning accuracy of about 6–13 cm and attitude accuracy of about 0.15° were achieved using a VLP-16 LiDAR. They also show that the method proposed in this paper is significantly better than the NDT method. For the long and narrow environment of an underground mine tunnel, high-resolution maps are also helpful for accurate and robust localization. [ABSTRACT FROM AUTHOR]

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel technique to estimate DCBs from GPS transmitters and receivers on-board Low Earth Orbit (LEO) satellites. The technique consists of obtaining the DCBs as residuals from the difference between the ionospheric combination of the code and the associated ionospheric delay. The ionospheric delay is computed with TOMION, a background-model-free ionospheric tomographic technique based on dual-frequency GPS carrier phase data only, and solved with a Kalman filter. Thus, DCBs are also estimated epoch-wise from the LEO Precise Orbit Determination (POD) GPS receiver as a secondary product. The results for GPS satellite DCBs, obtained exclusively from the three MetOp LEO POD GPS receivers over four consecutive weeks, are in full agreement (i.e., at the level of a few tenths of ns) with those reported independently with other techniques from hundreds of ground-based receivers exclusively, by JPL and CODE analysis centers. [ABSTRACT FROM AUTHOR]

3D Mapping-Aided (3DMA) Global Navigation Satellite System (GNSS) is a widely used method to mitigate multipath errors. Various research has been presented which utilizes 3D building model data in conjunction with ray-tracing algorithms to compute and predict satellites' visibility conditions and compute delays caused by signal reflection. To simulate, model and potentially correct multipath errors in highly dynamic applications, such as, e.g., autonomous driving, the satellite–receiver–reflector geometry has to be known precisely in a common reference frame. Three-dimensional building models are often provided by regional public or private services and the coordinate information is usually given in a coordinate system of a map projection. Inconsistencies in the coordinate frames used to express the satellite and user coordinates, as well as the reflector surfaces, lead to falsely determined multipath errors and, thus, reduce the performance of 3DMA GNSS. This paper aims to provide the needed transformation steps to consider when integrating 3D building model data, user position, and GNSS orbit information. The impact of frame inconsistencies on the computed extra path delay is quantified based on a simulation study in a local 3D building model; they can easily amount to several meters. Differences between the extra path-delay computations in a metric system and a map projection are evaluated and corrections are proposed to both variants depending on the accuracy needs and the intended use. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection. [ABSTRACT FROM AUTHOR]

The detection of drivable areas in off-road scenes is a challenging problem due to the presence of unstructured class boundaries, irregular features, and dust noise. Three-dimensional LiDAR data can effectively describe the terrain features, and a bird's eye view (BEV) not only shows these features, but also retains the relative size of the environment compared to the forward viewing. In this paper, a method called LRTI, which is used for detecting drivable areas based on the texture information of LiDAR reflection data, is proposed. By using an instance segmentation network to learn the texture information, the drivable areas are obtained. Furthermore, a multi-frame fusion strategy is applied to improve the reliability of the output, and a shelter's mask of a dynamic object is added to the neural network to reduce the perceptual delay caused by multi-frame fusion. Through TensorRT quantization, LRTI achieves real-time processing on the unmanned ground vehicle (UGV). The experiments on our dataset show the robustness and adaptability of LRTI to sand dust and occluded scenes. [ABSTRACT FROM AUTHOR]

Efficient dense reconstruction of objects or scenes has substantial practical implications, which can be applied to different 3D tasks (for example, robotics and autonomous driving). However, because of the expensive hardware required and the overall complexity of the all-around scenarios, efficient dense reconstruction using lightweight multi-view stereo methods has received much attention from researchers. The technological challenge of efficient dense reconstruction is maintaining low memory usage while rapidly and reliably acquiring depth maps. Most of the current efficient multi-view stereo (MVS) methods perform poorly in efficient dense reconstruction, this poor performance is mainly due to weak generalization performance and unrefined object edges in the depth maps. To this end, we propose EMO-MVS, which aims to accomplish multi-view stereo tasks with high efficiency, which means low-memory consumption, high accuracy, and excellent generalization performance. In detail, we first propose an iterative variable optimizer to accurately estimate depth changes. Then, we design a multi-level absorption unit that expands the receptive field, which efficiently generates an initial depth map. In addition, we propose an error-aware enhancement module, enhancing the initial depth map by optimizing the projection error between multiple views. We have conducted extensive experiments on challenging datasets Tanks and Temples and DTU, and also performed a complete visualization comparison on the BlenedMVS validation set (which contains many aerial scene images), achieving promising performance on all datasets. Among the lightweight MVS methods with low-memory consumption and fast inference speed, our F-score on the online Tanks and Temples intermediate benchmark is the highest, which shows that we have the best competitiveness in terms of balancing the performance and computational cost. [ABSTRACT FROM AUTHOR]

Sandy beaches and the nearshore environment are dynamic coastal systems characterized by sediment mobilization driven by alternating stormy and mild wave conditions. However, this natural behavior of beaches can be altered by coastal defense structures. Repeated surveys carried out with autonomous surface vehicles (ASVs) may represent an interesting tool for studying nearshore dynamics and testing the effects of mitigation strategies against erosion. We present a one-year experiment involving repeated stratigraphic and morpho-bathymetric surveys of a nearshore environment prone to coastal erosion along the Emilia-Romagna coast (NE Italy), the Lido di Dante beach, carried out between October 2020 and December 2021 using an ASV. Seafloor and subseafloor "snapshots" collected at different time intervals enabled us to delineate the seasonal variability and shed light on key controlling variables, which could be used to integrate and calibrate remote-sensing observations and modeling. The results demonstrated that repeated surveys could be successfully employed for monitoring coastal areas and represent a promising tool for studying coastal dynamics on a medium/short (years/months) timescale. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are equipped with multiple heterogeneous sensors and drive while processing data from each sensor in real time. Among the sensors, the global navigation satellite system (GNSS) is essential to the localization of the vehicle itself. However, if a GNSS-denied situation occurs while driving, the accident risk may be high due to the degradation of the vehicle positioning performance. This paper presents a cooperative positioning technique based on the lidar sensor and vehicle-to-everything (V2X) communication. The ego-vehicle continuously tracks surrounding vehicles and objects, and localizes itself using tracking information from the surroundings, especially in GNSS-denied situations. We present the effectiveness of the cooperative positioning technique by constructing a GNSS-denied case during autonomous driving. A numerical simulation using a driving simulator is included in the paper to evaluate and verify the proposed method in various scenarios. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevation data to form a 2.5D map image. The layered elevation images are given IDs in the Z plane to represent the height of the contained road features in meter-order whereas the elevation pixels indicate the cm-order of the road slope in the range of 200 cm. The layered intensity images are then created to describe the road surface in conjunction with the number of the layered elevation images and the corresponding pixel distributions. A significant map retrieval strategy during autonomous driving has been designed based on the LSM implementation tactic and the IDs in the XYZ plane. The system's reliability has been proved by a unique localization module to localize an autonomous vehicle in a challenging multilevel environment consisting of four stacked loops with an average accuracy of 5 cm in lateral, longitudinal and altitudinal directions. [ABSTRACT FROM AUTHOR]