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Studies in the area of mobile robotics have advanced in recent years, mainly due to the evolution of technology and the growing need for automated and dynamic solutions in sectors such as industry, transport, and agriculture. These devices are complex and the ideal method for localizing, mapping, and navigating autonomous mobile robots changes depending on the application. Thus, the general objective of this work is to propose a simultaneous localization and mapping method for autonomous mobile robots in indoor environments, using computer vision (CV) and Petri net (PN). A landmark was placed next to each door in the analyzed region and images were acquired as the rooms in the environment were explored. The algorithm processes the images to count and identify the doors. A transition is created in the PN for each door found and the rooms connected by these doors are represented by the places in the PN. Then, one of the doors is crossed, new images are obtained and the process is repeated until all rooms are explored. The algorithm generates a PN, which can be represented by an image file (.png) and a file with the extension.pnml. The results compare the layout of four environments with the respective generated PNs. Furthermore, six evaluation criteria are proposed for validating Petri nets as a topological map of environments. It is concluded that using PN for this purpose presents originality and potential innovation, being a SLAM technique for indoor environments, which demands low computational cost. [ABSTRACT FROM AUTHOR]

In recent years, some researchers have combined deep learning methods such as semantic segmentation with a visual SLAM to improve the performance of classical visual SLAM. However, the above method introduces the uncertainty of the neural network model. To solve the above problems, an improved feature selection method based on information entropy and feature semantic uncertainty is proposed in this paper. The former is used to obtain fewer and higher quality feature points, while the latter is used to correct the uncertainty of the network in feature selection. At the same time, in the initial stage of feature point selection, this paper first filters and eliminates the absolute dynamic object feature points in the a priori information provided by the feature point semantic label. Secondly, the potential static objects can be detected combined with the principle of epipolar geometric constraints. Finally, the semantic uncertainty of features is corrected according to the semantic context. Experiments on the KITTI odometer data set show that compared with SIVO, the translation error is reduced by 12.63% and the rotation error is reduced by 22.09%, indicating that our method has better tracking performance than the baseline method. [ABSTRACT FROM AUTHOR]

The present study focuses on the simultaneous localization and mapping (SLAM) system based on point and line features. Aiming to address the prevalent issue of repeated detection during line feature extraction in low-texture environments, a novel method for merging redundant line features is proposed. This method effectively mitigates the problem of increased initial pose estimation error that arises when the same line is erroneously detected as multiple lines in adjacent frames. Furthermore, recognizing the potential for the introduction of line features to prolong the marginalization process of the information matrix, optimization strategies are employed to accelerate this process. Additionally, to tackle the issue of insufficient point feature accuracy, subpixel technology is introduced to enhance the precision of point features, thereby further reducing errors. Experimental results on the European Robotics Challenge (EUROC) public dataset demonstrate that the proposed LR-SLAM system exhibits significant advantages over mainstream SLAM systems such as ORB-SLAM3, VINS-Mono, and PL-VIO in terms of accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude Unmanned Aerial Vehicles (UAVs), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one image. In contrast to perspective images with limited Field of View (FOV), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumer-grade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction from spherical images. As a result, this research provides a thorough survey of the state-of-the-art for 3D reconstruction from spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research. [ABSTRACT FROM AUTHOR]

When a mobile robot moves in an unknown environment, the emergence of Simultaneous Localization and Mapping (SLAM) technology becomes crucial for accurately perceiving its surroundings and determining its position in the environment. SLAM technology successfully addresses the issues of low localization accuracy and inadequate real-time performance of traditional mobile robots. In this paper, the Robot Operating System (ROS) robot system is used as a research platform for the 2D laser SLAM problem based on the scan matching method. The study investigates the following aspects: enhancing the scan matching process of laser SLAM through the utilization of the Levenberg–Marquardt (LM) method; improving the optimization map by exploring the traditional Hector-SLAM algorithm and 2D-SDF-SLAM algorithm, and employing the Weighted Signed Distance Function (WSDF) map for map enhancement and optimization; proposing a method for enhanced relocation using the Cartographer algorithm; establishing the experimental environment and conducting experiments utilizing the ROS robot system. Comparing and analyzing the improved SLAM method with the traditional SLAM method, the experiment proves that the improved SLAM method outperforms in terms of localization and mapping accuracy. The research in this paper offers a robust solution to the challenge of localizing and mapping mobile robots in unfamiliar environments, making a significant contribution to the advancement of intelligent mobile robot technology. [ABSTRACT FROM AUTHOR]

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) technology has garnered considerable attention as a pivotal component for the autonomous navigation of intelligent mobile vehicles. Integrating target detection and target tracking technology into SLAM enhances scene perception, resulting in a more resilient SLAM system. Consequently, this article presents a pose optimization algorithm based on image segmentation, coupled with object detection technology, to achieve superior multi-frame association feature matching. Subsequently, this paper proposes a method for selecting the most stable targets to better conduct pose optimization. Finally, experimental validation was conducted on five sequences from the TUM dataset. We conducted tracking performance experiments to demonstrate the necessity of selecting stable targets for pose optimization. Afterwards, we carried out a comprehensive comparison with the current state-of-the-art SLAM implementations in terms of accuracy and robustness. The average absolute trajectory error of our method in the dynamic benchmark datasets is ∼ 94.14% lower than that of ORB-SLAM2, ∼ 61.90% lower than that of RS-SLAM, and ∼ 80.89% lower than that of DS-SLAM. At the end of the experiment, the process performance of the proposed method is demonstrated. The experiments collectively showcase the system's capability to deliver outstanding results. [ABSTRACT FROM AUTHOR]

In many robotic applications, creating a map is crucial, and 3D maps provide a method for estimating the positions of other objects or obstacles. Most of the previous research processes 3D point clouds through projection-based or voxel-based models, but both approaches have certain limitations. This paper proposes a hybrid localization and mapping method using stereo vision and LiDAR. Unlike the traditional single-sensor systems, we construct a pose optimization model by matching ground information between LiDAR maps and visual images. We use stereo vision to extract ground information and fuse it with LiDAR tensor voting data to establish coplanarity constraints. Pose optimization is achieved through a graph-based optimization algorithm and a local window optimization method. The proposed method is evaluated using the KITTI dataset and compared against the ORB-SLAM3, F-LOAM, LOAM, and LeGO-LOAM methods. Additionally, we generate 3D point cloud maps for the corresponding sequences and high-definition point cloud maps of the streets in sequence 00. The experimental results demonstrate significant improvements in trajectory accuracy and robustness, enabling the construction of clear, dense 3D maps. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid advancement of information technology and intelligent systems, autonomous driving has garnered significant attention and research in recent years. Key technologies, such as Simultaneous Localization and Mapping (SLAM), Perception and Localization, and Scene Segmentation, have proven to be essential in this field. These technologies not only evolve independently, each with its own research focus and application paths, but also complement and rely on one another in various complex autonomous driving scenarios. This paper provides a comprehensive review of the development and current state of these technologies, along with a forecast of their future trends. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin. [ABSTRACT FROM AUTHOR]

With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments. [ABSTRACT FROM AUTHOR]

Place recognition is crucial for robot localization and loop closure in simultaneous localization and mapping (SLAM). Light Detection and Ranging (LiDAR), known for its robust sensing capabilities and measurement consistency even in varying illumination conditions, has become pivotal in various fields, surpassing traditional imaging sensors in certain applications. Among various types of LiDAR, spinning LiDARs are widely used, while non-repetitive scanning patterns have recently been utilized in robotics applications. Some LiDARs provide additional measurements such as reflectivity, Near Infrared (NIR), and velocity from Frequency modulated continuous wave (FMCW) LiDARs. Despite these advances, there is a lack of comprehensive datasets reflecting the broad spectrum of LiDAR configurations for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDARs, embodying spatiotemporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset supporting inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV)s and varying numbers of rays. The dataset covers diverse environments, from urban cityscapes to high-dynamic freeways, over a month, enhancing adaptability and robustness across scenarios. Notably, HeLiPR dataset includes trajectories parallel to MulRan sequences, making it valuable for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https://sites.google.com/view/heliprdataset. [ABSTRACT FROM AUTHOR]

Currently, Robot Operating System (ROS) provides multiple packages to implement different Simultaneous Localization and Mapping (SLAM) approaches. To effectively obtain sensor data, these packages use parameters whose values are set from prior knowledge and experience with robots and SLAM. In this paper, using a Multi-Objective Genetic Algorithm (MOGA) to optimize the values for these parameters is proposed. MOGA allows trade-offs between the objectives using Pareto dominance techniques. Three parameters from the RTAB-Map package are considered for optimization using three different MOGA mechanisms, Dominance Count, Dominance Rank and Switching Fitness. The quality of the map generated for every set of parameters is taken as the indicator of its performance. The number of corners, number of contours and the proportion of occupied cells in the map are used as quantitative measures of map quality. Finally, results obtained from testing the algorithm in simulation are used to test a Quanser QBot2 robot. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human–computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation. An 8-target steady-state visual evoked potential (SSVEP)-based BCI was implemented to enable direct control of the humanoid robot by the user. A Microsoft HoloLens was utilized to display visual stimuli for eliciting SSVEPs. Filter bank canonical correlation analysis (FBCCA), a training-free method, was used to detect SSVEPs in this study. By leveraging SLAM technology, the proposed system alleviates the need for frequent control commands transmission from the user, thereby effectively reducing their workload. Online results from 12 healthy subjects showed this developed BCI system was able to select a command out of eight potential targets with an average accuracy of 94.79%. The autonomous navigation subsystem enabled the humanoid robot to autonomously navigate to a destination chosen utilizing the proposed BCI. Furthermore, all participants successfully completed the experimental task using the developed system without any prior training. These findings illustrate the feasibility of the developed system and its potential to contribute novel insights into humanoid robots control strategies. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude Unmanned Aerial Vehicles (UAVs), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one image. In contrast to perspective images with limited Field of View (FOV), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumer-grade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction from spherical images. As a result, this research provides a thorough survey of the state-of-the-art for 3D reconstruction from spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.

Autonomous driving is identified as one of the key technologies for mining intelligence, with simultaneous localization and mapping (SLAM) technology serving as a key link to realize autonomous driving. To advance the development of SLAM technology in autonomous mining, this paper discusses the principles of SLAM technology, mature ground SLAM solutions, the current research status of mining SLAM, and future development trends. Based on the sensors employed in SLAM technology, the study analyzes the technical principles and corresponding frameworks from three aspects: vision, laser, and multi-sensor fusion. It is noted that visual and laser SLAM technologies, which utilize single cameras or LiDAR, are susceptible to environmental interference and cannot adapt to complex environments. Multi-sensor fusion SLAM emerges as the most effective solution. The research examines the status of mining SLAM technology, analyzing the applicability and research value of visual, laser, and multi-sensor fusion SLAM technologies in underground coal mines and open-pit mines. It concludes that multi-sensor fusion SLAM represents the optimal research approach for underground coal mines, while the research value of SLAM technology in open-pit mines is limited. Based on the challenges identified in underground SLAM technology, such as accumulated errors over time and activity range, adverse effects from various scenes, and the inadequacy of various sensors to meet the hardware requirements for high-precision SLAM algorithms, it is proposed that future developments in SLAM technology for autonomous mining should focus on multi-sensor fusion, solid-state solutions, and intelligent development.

Simultaneous localization and mapping (SLAM) is a critical technology for unmanned driving. Existing SLAM methods have the drawbacks of significant cumulative errors and drift in coal mine roadway environment. In this study, a roadway environment feature-assisted SLAM algorithm integrating inertial measurement unit (IMU) and LiDAR was proposed. IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. Pose transformation information from LiDAR odometry was obtained through point cloud registration, forming a LiDAR odometry constraint. Point clouds from roadway sidewalls and floor were extracted and fitted to planes, establishing environmental constraints. Using IMU pre-integration constraints, LiDAR odometry constraints, and environmental constraints, the algorithm applied factor graph optimization to achieve tight coupling between LiDAR and IMU, enabling high-precision 3D reconstruction of roadway scenes and accurate localization of autonomous vehicles. Simulation experiments showed that the absolute trajectory root mean square error (RMSE) of the roadway environment feature-assisted IMU-LiDAR integrated SLAM algorithm was 0.1162 m, and the relative trajectory RMSE was 0.0409 m, improving positioning accuracy compared to commonly used algorithms such as LeGO-LOAM and LIO-SAM. Based on the test results in a real environment, the algorithm provides excellent mapping performance with no drift or trailing, demonstrating strong environmental adaptability and robustness.

Loop closure detection (LCD) is crucial for simultaneous localization and mapping (SLAM). Current LiDAR-based methods focus on global scenes and often overlook the rich geometric features of crossroads. These scenes, with their irregular contours, traffic facilities, and vehicles, provide valuable information that is not adequately captured by single-dimensional descriptors, leading to weak discriminative ability. To address this, a novel descriptor called singular value decomposition scan context (SVDSC) is proposed, leveraging singular value decomposition (SVD) to extract geometric features of crossroads, enhancing recognition capability. An adaptive weighted similarity calculation method is also introduced to improve accuracy by considering local feature values. Experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), Jilin University (JLU), and self-collected crossroad datasets demonstrate the method’s superior performance in complex scenarios. Integrating this LCD algorithm into SLAM yields better mapping outcomes. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) refers to the common requirement for autonomous platforms to estimate their pose and map their surroundings. There are many robust and real-time methods available for solving the SLAM problem. Most are divided into a front-end, which performs incremental pose estimation, and a back-end, which smooths and corrects the results. A low-drift front-end odometry solution is needed for robust and accurate back-end performance. Front-end methods employ various techniques, such as point cloud-to-point cloud (PC2PC) registration, key feature extraction and matching, and deep learning-based approaches. The front-end algorithms have become increasingly complex in the search for low-drift solutions and many now have large configuration parameter sets. It is desirable that the front-end algorithm should be inherently robust so that it does not need to be tuned by several, perhaps many, configuration parameters to achieve low drift in various environments. To address this issue, we propose Simple Mapping and Localization Estimation (SiMpLE), a front-end LiDAR-only odometry method that requires five low-sensitivity configurable parameters. SiMpLE is a scan-to-map point cloud registration algorithm that is straightforward to understand, configure, and implement. We evaluate SiMpLE using the KITTI, MulRan, UrbanNav, and a dataset created at the University of Queensland. SiMpLE performs among the top-ranked algorithms in the KITTI dataset and outperformed all prominent open-source approaches in the MulRan dataset whilst having the smallest configuration set. The UQ dataset also demonstrated accurate odometry with low-density point clouds using Velodyne VLP-16 and Livox Horizon LiDARs. SiMpLE is a front-end odometry solution that can be integrated with other sensing modalities and pose graph-based back-end methods for increased accuracy and long-term mapping. The lightweight and portable code for SiMpLE is available at: https://github.com/vb44/SiMpLE. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of maintaining a stable mobile robot formation, steering and localizing all robots in an unknown dynamic environment consisting of multiple periodically moving objects, without the presence of a global positioning system or a robot tracking system. We propose a distributed observer such that each agent can estimate global positions of all mobile robots and that of moving landmarks in an unknown environment. By combining the proposed distributed observer with the distributed formation control and centroid tracking control law, we show that the formation shape can be maintained by utilizing its available relative measurements and the estimated relative measurements to its neighbors, and the group's centroid follows a desired trajectory. We present L2$$ {L}^2 $$ stability analysis of the closed‐loop system. Finally, we validate the proposed methods in a simulation result where a group of mobile robots can maintain a robust formation and maneuver in an unknown dynamic environment. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment. [ABSTRACT FROM AUTHOR]

Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps. [ABSTRACT FROM AUTHOR]

In autonomous driving of the mobile robot, the robot's current location should be identified first to plan and move a path to the target location. Accordingly, research on the robot's localization using GPS, 3D LiDAR, and Vision has been actively conducted. However, there is a limitation in that it is difficult to locate robots in indoor spaces where signals are disturbed by walls or ceilings, or in areas where sufficient environmental information cannot be obtained. This paper introduces the robot's position estimation method to overcome these environmental problems by using sensor fusion in an indoor tennis court. We propose a localization method that has low latency performance and high location accuracy through the use of Kalman filters to fuse data from wheel odometry and visual-inertial odometry. To evaluate its performance, this method was compared against wheel odometry, visual-inertial odometry, and LIO-SAM after the robot completed three rectangular paths. The resultant mean absolute errors in the x and y directions were 1.908 m and 0.707 m for wheel odometry, 1.169 m and 1.430 m for visual-inertial odometry, and 0.400 m and 0.383 m for LIO-SAM, respectively. In contrast, the wheel-visual-inertial odometry introduced in this study reported errors of 0.209 m and 0.103 m in the x and y directions, respectively, indicating superior accuracy compared to the other algorithms. This underscores the effectiveness of the proposed method in indoor environments where signals can be obstructed by walls or ceilings, or in areas lacking abundant environmental information. [ABSTRACT FROM AUTHOR]

Wheeled lifting robots have found widespread applications in various industrial and logistical environments. However, traditional robots are far from adequate in terms of visual perception capabilities. Additionally, their remote control methods suffer from inefficiencies, which tend to bring safety concerns. To address these issues, this work proposes an autonomous multi-sensor-enabled wheeled lifting robot system, i.e., AMSeWL-R, to facilitate remote autonomous operations. Specifically, AMSeWL-R integrates real-time simultaneous localization and mapping with object detection on a wheeled lifting robot. Additionally, a novel mobile-ROS interaction method is proposed to achieve real-time communication and control between a mobile device and a ROS host. Furthermore, a lightweight object detection algorithm based on YOLOv8, i.e., YOLOv8-R, is proposed to achieve faster detection. Experimental results validate the effectiveness of the AMSeWL-R system for accurately detecting objects and mapping its surroundings. Furthermore, TensorRT acceleration is employed during practical testing on a Jetson Nano to achieve real-time detection using the proposed YOLOv8-R, demonstrating its efficacy in real-world scenarios. [ABSTRACT FROM AUTHOR]

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html. [ABSTRACT FROM AUTHOR]

In this paper, we first study the projections onto the set of unit dual quaternions, and the set of dual quaternion vectors with unit norms. Then we propose a power method for computing the dominant eigenvalue of a dual quaternion Hermitian matrix. For a strict dominant eigenvalue, we show the sequence generated by the power method converges to the dominant eigenvalue and its corresponding eigenvector linearly. For a general dominant eigenvalue, we establish linear convergence of the standard part of the dominant eigenvalue. Based upon these, we reformulate the simultaneous localization and mapping problem as a rank-one dual quaternion completion problem. A two-block coordinate descent method is proposed to solve this problem. One block has a closed-form solution and the other block is the best rank-one approximation problem of a dual quaternion Hermitian matrix, which can be computed by the power method. Numerical experiments are presented to show the efficiency of our proposed power method. [ABSTRACT FROM AUTHOR]

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Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse, hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible and our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map's quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology and intelligent systems, autonomous driving has garnered significant attention and research in recent years. Key technologies, such as Simultaneous Localization and Mapping (SLAM), Perception and Localization, and Scene Segmentation, have proven to be essential in this field. These technologies not only evolve independently, each with its own research focus and application paths, but also complement and rely on one another in various complex autonomous driving scenarios. This paper provides a comprehensive review of the development and current state of these technologies, along with a forecast of their future trends.

For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of $ 2.211\% $ and a rotation error of $ 0.418\; ^{\circ}/100m $. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10$ \% $ intranslation error and 15$ \% $ in rotation error.

The drilling robot for rock-burst prevention is the key equipment for pressure relief in rock-burst mines, and its accurate map construction and stable navigation under complex working conditions are the basis and premise for realizing intelligent drilling operations. Based on the analysis of the causes of point cloud distortion of LiDAR and the defects of classical SLAM algorithm, a point cloud distortion correction method based on the IMU continuous time trajectory is proposed, a data fusion model of LiDAR and IMU is established, and the positioning and mapping process of drilling robot based on the IMU-LiDAR tight coupling is designed. A closed ramp model is built based on the characteristics of coal mine pressure relief roadways, and the simulation analysis of the mapping effect is conducted. The results show that the proposed mapping algorithm outperforms existing commonly used methods in terms of positioning accuracy and trajectory error. On this basis, a dynamic path planning method based on the improved artificial potential field and rapidly-exploring random tree is proposed, and a path planning and navigation fusion scheme suitable for drilling robot is designed. Two simulation motion scenarios are then designed, and the results indicate that the proposed path planning method has a better comprehensive performance in terms of average path length, average running time, and average number of generated nodes in both global and dynamic path planning. In order to further verify the practicality of the positioning and navigation method, multiple comparative experiments are conducted in a simulated roadway, ground experimental base and underground pressure relief roadway, and the results indicate that after tightly coupling IMU data with LiDAR data, the positioning accuracy of the proposed method is significantly improved and has a superior positioning performance in feature degradation scenarios. In addition, the planning path has better performance in terms of computational efficiency and cost. The results prove the feasibility and superiority of the proposed positioning and navigation method in various scenarios.

Simultaneous localization and mapping (SLAM) and 3D reconstruction have numerous applications for indoor ground wheeled robots such as floor sweeping and food delivery. To advance research in leveraging semantic information and multi-sensor data to enhance the performances of SLAM and 3D reconstruction in complex indoor scenes, we propose a novel and complex indoor dataset named CID-SIMS, where semantic annotated RGBD images, inertial measurement unit (IMU) measurements, and wheel odometer data are provided from a ground wheeled robot viewpoint. The dataset consists of 22 challenging sequences captured in nine different scenes including office building and apartment environments. Notably, our dataset achieves two significant breakthroughs. Firstly, semantic information and multi-sensor data are provided meanwhile for the first time. Secondly, GeoSLAM is utilized for the first time to generate ground truth trajectories and 3D point clouds within two-centimeter accuracy. With spatial-temporal synchronous ground truth trajectories and 3D point clouds, our dataset is capable of evaluating SLAM and 3D reconstruction algorithms in a unified global coordinate system. We evaluate state-of-the-art SLAM and 3D reconstruction approaches on our dataset, demonstrating that our benchmark is applicable. The dataset is publicly available on https://cid-sims.github.io. [ABSTRACT FROM AUTHOR]