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Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while mapping the landmarks in the environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle the entire SLAM problem in a theoretically optimal manner. The complexity of the three proposed SLAM filters is progressively reduced while sustaining high accuracy by deriving SLAM density approximation with the marginalization of nuisance parameters (either vehicle state or data association). Firstly, the PMBM SLAM filter serves as the foundation, for which we provide the first complete description based on a Rao-Blackwellized particle filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy. Finally, using the same auxiliary variable argument, we derive a marginalized PMB SLAM filter, which avoids particles and is instead implemented with a low-complexity cubature Kalman filter. We evaluate the three proposed SLAM filters in comparison with the probability hypothesis density (PHD) SLAM filter in 5G mmWave vehicular networks and show the computation-performance trade-off between them. [ABSTRACT FROM AUTHOR]

3D LiDAR maps are playing a more and more important role in intelligent vehicle localization. In this paper, the proposed SeqPolar localization method consists of two parts. The first part is a novel node-based LiDAR map representation method, termed polarized LiDAR map (PLM), and the second is a map matching algorithm based on a second-order hidden Markov model (HMM2) for vehicle localization. Unlike existing 3D LiDAR map, which is usually constructed by accumulating 3D LiDAR clouds collected at different times, the proposed polarized LiDAR map is generated from a set of nodes. Each node consists of three elements: a polarized LiDAR image, scene features extracted from the polarized LiDAR image, and sensor pose. The polarized LiDAR image encodes 3D coordinates and reflectivities of the 3D LiDAR cloud using a multi-channel image format, a more concise, straightforward, and structured representation of the 3D LiDAR cloud. In the localization step, we propose an HMM2-based method to match a sequence of input polarized images within the map nodes and find the nearest node from the PLM. Afterward, the vehicle is readily localized from the matched map node with 3D registration. The proposed SeqPolar localization method has been validated with the actual field dataset and the public KITTI database. Experimental results demonstrate that the proposed HMM2-based matching method can achieve up to 98% accuracy to find the nearest node from PLM. Moreover, the SeqPolar localization method based on the pre-built PLM can achieve 30-centimeter localization accuracy in average on both test datasets. [ABSTRACT FROM AUTHOR]

Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles. [ABSTRACT FROM AUTHOR]

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient $\ell _1$ -regularized least-squares (LS) approach to obtain sparse range–angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks. [ABSTRACT FROM AUTHOR]

Indoor drone or Unmanned Aerial Vehicle (UAV) operations, automated or with pilot control, are an upcoming and exciting subset of drone use cases. Automated indoor flights tighten the requirements of stability and localization accuracy in comparison with the classic outdoor use cases which rely primarily on (RTK) GNSS for localization. In this paper the effect of multiple sensors on 3D indoor position accuracy is investigated using the flexible sensor fusion platform OASE. This evaluation is based on real-life drone flights in an industrial lab with mm-accurate ground truth measurements provided by motion capture cameras, allowing the evaluation of the sensors based on their deviation from the ground truth in 2D and 3D. The sensors under consideration for this research are: IMU, sonar, SLAM camera, ArUco markers and Ultra-Wideband (UWB) positioning with up to 6 anchors. The paper demonstrates that using this setup, the achievable 2D (3D) indoor localization error varies between 4.4 cm and 21 cm (4.9 cm and 67.2 cm) depending on the selected set of sensors. Furthermore, cost/accuracy trade-offs are included to indicate the relative importance of different sensor combinations depending on the (engineering) budget and use case. These lab results were validated in a Proof of Concept deployment of an inventory scanning drone with more than 10 flight hours in a 65 000 m2 warehouse. By combining lab results and real-life deployment experiences, different subsets of sensors are presented as a minimal viable solution for three different indoor use cases considering accuracy and cost: a large drone with little weight- and cost restrictions, one or more medium sized drones, and a swarm of weight and cost restricted nano drones. [ABSTRACT FROM AUTHOR]

Vision-based simultaneous localization and mapping (SLAM) has been indispensable for autonomous vehicles. However, in challenging environments, such as sparsely textured parking lots, the state-of-the-art methods often fail to obtain sufficiently reliable features for robust localization and consistent mapping. This paper proposes a multi-level visual information hierarchical fusion SLAM framework based on a multi-camera system. High-level features like object edges are extracted by different segmentation methods. With the guidance of high-level information, low-level key points are selected. The hierarchical fusion structure consists of two parts. High-level visual information is loosely fused with the wheel encoder to estimate initial data association. Then, low-level feature points of multiple views are tightly fused to facilitate robust localization. With such a structure, a consistent map can be built even under the condition that one view is lost. In addition, the lost view can be reinitialized in the single map with other information instead of waiting for re-localization or creating additional maps. Real-time experiments in autonomous parking scenario validate the robustness and consistency of the proposed method. [ABSTRACT FROM AUTHOR]

Visual 3D perception is a key interface between vehicles and the environment which provides rich perception information for autonomous vehicles. In vehicular visual 3D perception, camera position uncertainty can often degrade the perception and localization performance, and the information coupling and propagation among 3D points and cameras exist ubiquitously. However, these factors do not receive the deserving concern in previous algorithms which mainly focus on the consideration of efficiency. In this paper, we develop a statistical framework for visual 3D perception in vehicular networks from the perspective of information gains. Specifically, we first derive the influence of camera position uncertainty on perception and localization quality with geometric interpretations given by the information ellipsoid, and also present the perception loss caused by uncertainty in the camera deployment application. Then we determine the information coupling and propagation among 3D points and cameras in the vehicular network, propose the visual information graph to characterize the coupling, and derive the hierarchical structure in propagation. Moreover, we also propose the equivalent circuit for the quasi-tree network to interpret the hierarchical structure and derive the blocking and the end-link effects for network reduction. Our results provide guidelines for the development of efficient uncertainty- and coupling-aware visual 3D perception techniques in vehicular networks. [ABSTRACT FROM AUTHOR]

Articulation angle sensing is an essential component of manoeuvrability and stability control systems for articulated heavy goods vehicles, particularly long combination vehicles. Existing solutions to this sensing task are limited by reliance on trailer modifications or information or by measurement accuracy, or both, restricting commercial adoption. In this paper we present a purely tractor-based sensor concept comprising a rear-facing camera and the parallel tracking and mapping (PTAM) image processing algorithm. The system requires no prior knowledge of or modifications to the trailer, is compatible with planar and non-planar trailer shapes, and with multiply-articulated vehicle combinations. The system is validated in full-scale vehicle tests on both a tractor semi-trailer combination and a truck and full-trailer combination, demonstrating robust performance in a number of conditions, including trailers with non-planar geometry and with minimal visual features. Average RMS measurement errors of 1.19, 1.03 and 1.53 degrees were demonstrated for the semi-trailer and full-trailer (drawbar and semi-trailer) respectively. This compares favourably with the state-of-the-art in the published literature. A number of improvements are proposed for future development based on the observations in this research. [ABSTRACT FROM AUTHOR]

This paper addresses vehicle positioning, a topic whose importance has risen dramatically in the context of future autonomous driving systems. While classical methods that use GPS and/or beacon signals from network infrastructure for triangulation tend to be sensitive to multi-paths and signal obstruction, our method exhibits robustness with respect to such phenomena. Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multi-path so as to improve (single) vehicle positioning. Here, we propose a cooperative mapping approach which builds upon the Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM not only exploits the stationary nature of many reflecting objects around the vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The key intuition behind our method is the exploitation for the first time of the correlation between reflectors around multiple neighboring vehicles. An algorithm is derived for reflector selection and estimation, combined with a team particle filter (TPF) so as to achieve high precision simultaneous multiple vehicle positioning. We obtain large improvement over the single-vehicle positioning scenario, with gains being already noticeable for moderate vehicle densities, such as over 40% improvement for a vehicle density as low as 4 vehicles in 132 meters’ length road. [ABSTRACT FROM AUTHOR]

Intelligent mobile vehicles asks for the ability to learn to comprehend immediate surroundings similar to human cognition and autonomously navigate in an unknown scene. Although simultaneous localization and mapping (SLAM) can construct a geometrically surrounding map, the map contains little semantic information. This paper presents a novel stereo visual-inertial system with loop closure detection based on a semantic-topological map framework. In our system, a hybrid 3D point cloud semantic-topological mapping framework is used to realize autonomous navigation simply by providing a map for path planning and meanwhile for storing semantic information under a dynamic environment. A stereo visual-inertial system can obtain more accurate visual-inertial odometry than a monocular system, and a visual-inertial system is capable of real-time map correction when the localization module detects a loop closure. This paper adopts state-of-the-art Mask R-CNN components to obtain 2D semantic information and project it to a semi-dense 3D semantic-topological map built by stereo visual-inertial SLAM. We perform experimental evaluation of our proposed system on the EuRoC MAV datasets and we also compare it with other competitive methods. Our results demonstrate that the proposed method is more effective than the other competitive methods. [ABSTRACT FROM AUTHOR]

Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Realtime localization and mapping in a cluttered and noisy indoor environment is a major problem in autonomous unmanned ground vehicle (UGV) navigation. this article proposes a Heuristic Monte Carlo algorithm (HMCA) based on the Monte Carlo localization and Discrete Hough Transform (DHT) to build an autonomous navigation system. Specifically, a generalized map-processing method is first presented for the Hough Transform Algorithm (HTA), which can extract and cluster important map feature information and preserve the low computational complexity of real-time processing. Then a set of relative rotation angles and corresponding spatial displacements of the robot are obtained by constructing hough spatial energy spectrum correlation functions as global and local 2D occupancy grid maps (OGMs). Finally, the result is used as the guiding particle set of the Monte Carlo location algorithm. In a simulated scene and a real scene, the algorithm is tested several times, the adequacy of the algorithm is verified, and a map with good self-positioning performance is constructed. [ABSTRACT FROM AUTHOR]

The technology for simultaneous localization and mapping (SLAM) has been well investigated with the rising interest in autonomous driving. Visual odometry (VO) is a variation of SLAM without global consistency for estimating the position and orientation of the moving object through analyzing the image sequences captured by associated cameras. However, in the real-world applications, we are inevitably to experience drift error problem in the VO process due to the frame-by-frame pose estimation. The drift can be more severe for monocular VO compared with stereo matching. By jointly refining the camera poses via several local keyframes and the coordinate of 3D map points triangulated from extracted features, bundle adjustment (BA) can mitigate the drift error problem only to some extent. To further improve the performance, we introduce a traffic sign feature-based joint BA module to eliminate and relieve the incrementally accumulated pose errors. The continuously extracted traffic sign feature with standard size and planar information will provide powerful additional constraints for improving the VO estimation accuracy through BA. Our framework can collaborate well with existing VO systems, e.g., ORB-SLAM2, and the traffic sign feature can also be replaced with feature extracted from other size-known planar objects. Experimental results by applying our traffic sign feature-based BA module show an improved vehicular localization accuracy compared with the state-of-the-art baseline VO method. [ABSTRACT FROM AUTHOR]

A semantic map, which can provide important data to facilitate a drone's understanding to the environment, is critical for a fully autonomous drone. However, recent methods for producing such a map implemented on small drones that use low-/middle-grade processors and graphics processing units can hardly achieve real-time performance. In addition, few existing methods can reconstruct semantic planes based on a sparse depth map, which can greatly reduce computational load while encountering challenges in semantic reconstruction. To address this problem, this paper presents a novel on-board approach, called sparse fusion, which demonstrates real-time reconstruction of a semantic plane on a self-designed small drone. The approach combines the sparse depth map derived from a visual–inertial simultaneous localization and mapping with the semantic labels derived from a convolutional neural network for each frame using sparse fusion. Our proposed local plane optimization function greatly improves the accuracy of the semantic plane. Experimental results on various scenarios demonstrate that our sparse fusion module running on the drone platform can update the semantic plane within 1 ms, which is faster and achieves greater accuracy than the state-of-the-art real-time semantic reconstruction method. We also conducted experiments in real environments to demonstrate the performance of our method. [ABSTRACT FROM AUTHOR]

This paper proposes the posterior linearization backward simultaneous localization and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellized Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearization to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localization and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks. [ABSTRACT FROM AUTHOR]

We propose a novel 6-degree-of-freedom (DoF) visual simultaneous localization and mapping (SLAM) method based on the structural regularity of man-made building environments. The idea is that we use the building structure lines as features for localization and mapping. Unlike other line features, the building structure lines encode the global orientation information that constrains the heading of the camera over time, eliminating the accumulated orientation errors and reducing the position drift in consequence. We extend the standard extended Kalman filter visual SLAM method to adopt the building structure lines with a novel parameterization method that represents the structure lines in dominant directions. Experiments have been conducted in both synthetic and real-world scenes. The results show that our method performs remarkably better than the existing methods in terms of position error and orientation error. In the test of indoor scenes of the public RAWSEEDS data sets, with the aid of a wheel odometer, our method produces bounded position errors about 0.79 m along a 967-m path although no loop-closing algorithm is applied. [ABSTRACT FROM PUBLISHER]

The articles in this special section focus on the use of heterogeneous technologies for indoor Localization, tracking, and mapping. Accurate availability of position opens the way to enhanced system performance and functionalities, such as automated guided vehicles and people movers. While satellite-based positioning systems (generally referred to as Global Navigation Satellite Systems, namely GNSS, including GPS, GLONASS, and Galileo) provide position information that is accurate enough for most applications in outdoor environments, they usually do not work well in close or harsh environments. Therefore, other localization technologies must be considered. In some scenarios, such as inside buildings and unexplored caves, even the map of the environment might not be available. Thus, the localization processes have to be executed in parallel to mapping [i.e., simultaneous localization and mapping (SLAM)], making the problem even more challenging. Different from outdoor applications and the reliance on GNSS, for indoor applications, there is no single dominant technology that can meet a sufficiently wide range of requirements. As a result, a plethora of solutions has been investigated over the past decade. A current trend in indoor localization and tracking is to use a combination of standards, low cost, and already-deployed technologies and resort to statistical fusion of the information they provide. [ABSTRACT FROM PUBLISHER]