Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high‐bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate ...extremely aggressive robotic motions. Point‐LIO has two key novelties. The first one is a point‐by‐point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high‐frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in‐frame motion distortion. The second one is a stochastic process‐augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real‐world experiments are conducted for performance evaluation. Overall, Point‐LIO is capable to provide accurate, high‐frequency odometry (4–8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s−1) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point‐LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point‐LIO are demonstrated, one is a racing drone and the other is a self‐rotating unmanned aerial vehicle, both have aggressive motions.
A high‐bandwidth light detection and ranging (LiDAR)‐inertial system (LIO) is presented. The system updates the state at the sampling time of each LiDAR point or inertial measurement unit (IMU) measurement without accumulating a frame. The system is able to output a high‐rate (4–8 kHz), high‐bandwidth (over 150 Hz) odometry and handles extremely aggressive motions where IMU saturates.
Determining the extrinsic parameter between multiple light detection and rangings (LiDARs) and cameras is essential for autonomous robots, especially for solid-state LiDARs, where each LiDAR unit has ...a very small field-of-view (FoV), and multiple units are often used collectively. The majority of extrinsic calibration methods are proposed for 360° mechanical spinning LiDARs where the FoV overlap with other LiDAR or camera sensors is assumed. A few research works have been focused on the calibration of small FoV LiDARs and cameras nor on the improvement of the calibration speed. In this work, we consider the problem of extrinsic calibration among small FoV LiDARs, and cameras, with the aim to shorten the total calibration time and further improve the calibration precision. We first implement an adaptive voxelization technique in the extraction and matching of LiDAR feature points. Such a process could avoid the redundant creation of <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-d trees in LiDAR extrinsic calibration and extract LiDAR feature points in a more reliable and fast manner than existing methods. We then formulate the multiple LiDAR extrinsic calibration into a LiDAR bundle adjustment (BA) problem. By deriving the cost function up to second order, the solving time and precision of the nonlinear least square problem are further boosted. Our proposed method has been verified on data collected in four targetless scenes and under two types of solid-state LiDARs with a completely different scanning pattern, density, and FoV. The robustness of our work has also been validated under eight initial setups, with each setup containing 100 independent trials. Compared with the state-of-the-art methods, our work has increased the calibration speed 15 times for LiDAR-LiDAR extrinsic calibration (averaged result from 100 independent trials) and 1.5 times for LiDAR-camera extrinsic calibration (averaged result from 50 independent trials) while remaining accurate. To benefit the robotics community, we have also open-sourced our implementation code on GitHub.
In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can ...achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges' richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open sourced our code on GitHub<xref ref-type="fn" rid="fn1"> 1 to benefit the community.
This letter proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane feature that enables the probabilistic ...representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Experiments on indoor and unstructured outdoor environments with solid-state LiDAR and non-repetitive scanning LiDAR further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns (see our attached video 1 ). Our codes and dataset are open-sourced on Github 2
Accurate and robust place recognition is essential for robot navigation, yet achieving full pose invariance and high performance across diverse scenes remains challenging. In this work, we propose a ...novel global and local combined descriptor named binary triangle combined (BTC) descriptor. We first extract the keypoints of a point cloud by projecting the points to planes extracted therein. Any three keypoints form a unique triangle, with the lengths of its sides constituting a triangle descriptor that captures the global appearance of the point cloud. Thanks to the distinct shape of a triangle given three side lengths, the similarity between two triangles and their vertices (i.e., keypoints) correspondence can be naturally determined from the side lengths of the triangle descriptors. The matched triangle pairs evaluate the appearance similarity between two point clouds, while the vertices' correspondence enables accurate estimation of their relative pose; both are crucial for the place recognition task. To enhance the accuracy of triangle matching, BTC introduces a binary descriptor, which describes the point distribution neighboring each keypoint. The local geometry information encoded by the binary descriptor augments descriptiveness and discriminativeness to the triangle descriptor. Collectively, the two descriptors achieve both global and local descriptions of the environment with high accuracy, efficiency, and robustness. We extensively compare the proposed BTC descriptor against state-of-the-art methods (e.g., Scan Context, LCD-Net) on a wide range of datasets collected using different types of LiDAR sensors (spinning LiDARs and nonrepetitive scanning LiDARs) in various environments (urban, campus, forest, park, and mountain). The quantitative results demonstrate that BTC exhibits greater adaptability and significant improvement in precision compared to its counterparts, especially in challenging cases with large viewpoint variations (e.g., reverse direction, large translation, and/or rotation).
In this article, we propose a novel light detection and ranging (LiDAR)(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real time. This ...proposed framework termed immediately meshing (ImMesh) comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module first utilizes the preprocessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The primary contribution of this work is the meshing module, which represents a scene by an efficient voxel structure, performs fast finding of voxels observed by new scans, and incrementally reconstructs triangle facets in each voxel. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3-D points to a 2-D local plane contained in the voxel, and then executes the meshing operation with pull, commit, and push steps for incremental reconstruction of triangle facets. To the best of authors' knowledge, this is the first work in the literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration.
Abstract This paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from ...the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps .
The fusion of 3D LiDAR and 2D camera data has gained popularity in the field of robotics in recent years. Extrinsic calibration is a critical issue in sensor data fusion. Poor calibration can lead to ...corrupt data and system failure. This letter introduces a method based on photometric consistency for detecting and recalibrating camera LiDAR miscalibrations in arbitrary environments, online and without the need for calibration targets or manual work. We make the assumption that, with correct extrinsic parameters and accurate LiDAR pose estimation, the projections of each LiDAR point onto different camera images will have similar photometric values. By utilizing covisibility information, an error term based on the aforementioned photometric consistency assumption is proposed, enabling the detection and correction of miscalibration. Multiple experiments were conducted using real-world data sequences.
Localization and Mapping Systems
In article number 2200459, Dongjiao He, Wei Xu, and colleagues present a high‐bandwidth LiDAR‐inertial system (LIO), which updates the state at the sampling time of ...each LiDAR point or IMU measurement without accumulating a frame. The system is motion distortion free and is able to output a high‐rate (4 kHz – 8 kHz), high‐bandwidth (over 150 Hz) odometry and handles extremely aggressive motions where IMU saturates.
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