LiDAR-based Global Localization (LGL) is an essential ingredient for autonomous robots. However, existing LGL methods typically consider only partial information (e.g., geometric features) from LiDAR observations or are designed for homogeneous LiDAR sensors, overlooking the uniformity in LGL. In this work, a uniform LGL method is proposed, termed UniLGL, which simultaneously achieves spatial and material uniformity, as well as sensor-type uniformity. The key idea of the proposed method is to encode the complete point cloud, which contains both geometric and material information, into a pair of Bird's Eye View (BEV) images (i.e., a spatial BEV image and an intensity BEV image), thereby transforming the LGL problem into a cascaded LiDAR Place Recognition (LPR) and pose estimation problem from the perspective of image fusion. An end-to-end multi-BEV fusion network is designed to extract uniform features, equipping UniLGL with spatial and material uniformity. To ensure robust LGL across heterogeneous LiDAR sensors, a viewpoint invariance hypothesis is introduced, which replaces the conventional translation equivariance assumption commonly used in existing LPR networks and supervises UniLGL to achieve sensor type uniformity in both global descriptors and local feature representations. Moreover, UniLGL introduces a pipeline that leverages a pre-trained single-image Vision Foundation Model (VFM) for feature extraction to enhance the multi-BEV fusion LPR network, enabling strong generalization with only a few LiDAR data for fine-tuning. Finally, based on the mapping between local features on the 2D BEV image and the point cloud, a robust global pose estimator is derived that determines the global minimum of the global pose on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}(3)$</tex-math></inline-formula> without requiring additional registration. To validate the effectiveness of the proposed uniform LGL, extensive benchmarks are conducted in real-world environments, and the results show that the proposed UniLGL is demonstratively competitive compared to other State-of-the-Art (SOTA) LGL methods. Furthermore, UniLGL has been deployed on diverse platforms, including full-size trucks and agile Micro Aerial Vehicles (MAVs), to enable high-precision localization and mapping as well as multi-MAV collaborative exploration in port and forest environments, demonstrating the applicability of UniLGL in industrial and field scenarios. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shenhm516/UniLGL</uri>.

In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. However, single LiDAR odometry often faces hardware failures and degradation in practical scenarios, and the continuous-time measurement characteristic is constantly neglected by existing LiDAR odometry. This motivates us to develop a continuous-time Multi-LiDAR Odometry (MLO) method, namely CT-MLO, which can realize accurate and real-time state estimation using multi-LiDAR measurements through a continuous-time perspective. Due to the advantageous continuous-time formulation, each LiDAR point in a point stream can query the corresponding continuous-time trajectory within its time instants. Additionally, a decentralized multi-LiDAR synchronization scheme is devised to combine points from separate LiDARs into a single point cloud without the need for primary LiDAR assignment. With the detailed derivation of the analytic Jacobians for continuous-time LiDAR observation, the proposed method integrates synchronization, continuous-time estimation, and voxel map management within a Kalman filter framework, which can achieve real-time state estimation with only a few linear iterations. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world autonomous driving experiments. The results demonstrate that the proposed CT-MLO can achieve high-accuracy continuous-time state estimations in real-time and is demonstratively competitive compared to other State-of-the-Art (SOTA) methods.

As a new sensor, 4D radar (x, y, z, velocity) has great potential for V2X, due to its 3D point cloud, direct doppler velocity output, long distance ranging, low-cost, and more importantly, robust perception in all weathers. However, the extrinsic calibration of multiple long baseline 4D radars is rarely researched in V2X, which is the key to fuse multi-radars. The main reasons are three-folds: (1) New sensor. Thus, it is not surprising that little related work can be found. (2) Long baseline and large viewpoint-difference. Current works are mainly focused on unmanned vehicles, which is short baseline and small viewpoint-difference. (3) Sparse, noisy, and very cluttered 4D radar point cloud. Thus, it is challenging to rapidly and accurately locate the target and extract the feature. In this paper, LB-R2R-Calib (Long Baseline Radar to Radar extrinsic Calibration) is proposed to address these problems. The novelties are: (1) A new target is introduced: an eight-quadrant corner reflector enclosed by a foam sphere. The benefit is the target center is a viewpoint-invariant feature. Thus, it is ideal for large viewpoint-difference calibration. (2) A new feature extraction algorithm is proposed to rapidly locate the target and extract the target center from a very cluttered point cloud, as we observed some important characteristics of 4D radar. Experiments with two 4D radars in real environments with four configurations demonstrate our method is highly accurate and robust.

The localization of autonomous robots in modern enclosed or semi-enclosed environments, such as office/hotel/hospital, supermarket, and indoor car park environments where GPS signals are severely challenged, remains a bottleneck for the deployment of fully autonomous mobile systems. Existing infrastructure-based (e.g., QR codes, RFID) localization methods are troubled by high maintenance cost and inflexibility issues, while onboard sensors-based solutions (e.g., LiDAR/camera-based) suffer from the ambiguous geometric features and view obstructions from crowded dynamic obstacles (e.g., pedestrians). Magnetic field (MF)-based localization has been gradually utilized in recent years due to its independence from positioning infrastructures and geometric features, thus making it ideal for applications such as service robots and security robots. Magnetic map building serves as the basis and prerequisite component for MF-based localization tasks. The well-acknowledged Gaussian Process Regression (GPR) method can be implemented to build magnetic maps but with heavy computational burdens. Thus in this paper, we propose an efficient and accurate magnetic mapping system based on a novel Sliding-GPR (i.e., S-GPR) method, and evaluate different magnetic mapping methods. A unique region-of-interest (ROI) selection technique and a down/up-sampling method are proposed for the S-GPR to dramatically decrease the computational time while maintaining the mapping accuracy. Extensive experiments in a high-fidelity simulated warehouse and real-world car park environments show that our proposed S-GPR mapping method has exhibited the highest accuracy and relatively low computational time compared with the SOTA magnetic mapping methods.

In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the preinstalled infrastructures suffer from inflexibilities and high maintenance costs. This poses an interesting problem of how to develop a drift-free localization system without using the preinstalled infrastructures. In this paper, an infrastructure-free and drift-free localization system is proposed using the ambient magnetic field (MF) information, namely IDF-MFL. IDF-MFL is infrastructure-free thanks to the high distinctiveness of the ambient MF information produced by inherent ferromagnetic objects in the environment, such as steel and reinforced concrete structures of buildings, and underground pipelines. The MF-based localization problem is defined as a stochastic optimization problem with the consideration of the non-Gaussian heavy-tailed noise introduced by MF measurement outliers (caused by dynamic ferromagnetic objects), and an outlier-robust state estimation algorithm is derived to find the optimal distribution of robot state that makes the expectation of MF matching cost achieves its lower bound. The proposed method is evaluated in multiple scenarios1, including experiments on high-fidelity simulation, and real-world environments. The results demonstrate that the proposed method can achieve high-accuracy, reliable, and real-time localization without any pre-installed infrastructures.

Accurate global localization is an essential ingredient for autonomous mobile robots (AMRs) operating in enclosed or partially enclosed repetitive environments (e.g., office corridors, industrial warehouses, transportation centers). In such environments, the Global Navigation Satellite System (GNSS) signals are unreliable or severely degraded. The highly ambiguous structures in such challenging scenarios would also lead the ordinary geometric feature-based LiDAR/visual localization methods to fail. The ambient magnetic field (MF) has exhibited high distinctiveness at different location, which makes it a viable alternative for infrastructure-free AMR localization. However, few of the previous research has been focused on the orientation-dependency and similar-sequential-route limitations of MF-based localization. Thus, this paper proposes a novel probabilistic global localization system with 2-D LiDAR and rotation-invariant magnetic field for AMRs operating in challenging repetitive and ambiguous environments. The proposed localization system mainly consists of: 1) Two-step Initialization: laser distance and MF sequence based matching, and 2) MF-based Pose Tracking: recursive multi-dimensional MF sequence based matching. Extensive experimental results demonstrate the advantageous localization performances of the proposed localization system over the existing methods.

In V2X (Vehicle-to-Everything), one important work is to extrinsically calibrate multiple 3D LiDARs, which are mounted with a long baseline and large viewpoint-difference at the road-side. Current solutions either require a specific target being set up (e.g., a sphere), or require specific features existing in the environment (e.g., mutually orthogonal planes). However, it is time-consuming, sometimes even inconvenient, to set up specific targets, e.g., at busy intersections and highways. Furthermore, specific features do not always exist in the traffic scenario. Thus, the current solutions are not feasible. To address this problem, a novel extrinsic calibration method is proposed in this paper, namely LB-L2L-Calib 2.0. It is the 2.0 version of our previous work. The novelties are: 1) We propose to use the easily accessible objects on the road as features for calibration (i.e., the vehicles). Thus, it is not necessary to set up any specific targets and we do not need to worry whether specific features exist or not. The key point is we observed that the 3D bounding box centers of the vehicles are viewpoint-invariant from different viewpoints, which makes them ideal features for long baseline and large viewpoint-difference calibration. 2) To establish correct correspondence between the bounding box centers detected from different LiDARs, we propose an exhaustive searching strategy. It can robustly output correct correspondence. Extensive experiments are performed in three scenarios (simulation: intersection, real: carpark and highway), with two types of LiDAR (Velodyne and Livox), demonstrating that LB-L2L-Calib 2.0 is robust, effective, and accurate.

Multi-LiDAR system is an important part of V2X (Vehicle to Everything) to enhance the perception information for unmanned vehicles. To fuse the information from multiple 3D LiDARs, accurate extrinsic calibration between the LiDARs is essential. However, the existing multi-LiDAR calibration methods mainly focus on short baseline scenarios, where multiple LiDARs are closely mounted on a single platform (e.g., an unmanned vehicle). Besides, most methods typically use a planar target for calibration. Some of the methods require the motion of the multi-LiDAR system. The above conditions severely limit the application of these methods to V2X, where LiDARs are non-movable, the baseline and viewpoint difference between the LiDARs can be very large. In order to meet these challenges, we propose an accurate and robust extrinsic calibration method for long baseline multi-LiDAR systems, named LB-L2L-Calib (Large Baseline LiDAR to LiDAR extrinsic Calibration). (1) We use a sphere as the calibration target for multiple LiDARs with large viewpoint difference, leveraging the viewpoint-invariance of the sphere. (2) A improved sphere detection and sphere center estimation strategy is introduced to detect and extract the sphere center from a cluttered point cloud in large-scale outdoor scenario. (3) A extrinsic parameter regression scheme is introduced. Both simulation and real experiments demonstrate that LB-L2L-Calib is highly accurate and robust. Quantitative results show that the rotation and translation error is less than 0.01m and 0.01° (in simulation, Gauss noise 0.03m, the distance and viewpoint difference between two LiDARs is more than 30m and 90°).