1
|
Shi B, Lin W, Ouyang W, Shen C, Sun S, Sun Y, Sun L. BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors. SENSORS (BASEL, SWITZERLAND) 2024; 24:5554. [PMID: 39275468 PMCID: PMC11398242 DOI: 10.3390/s24175554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 08/22/2024] [Accepted: 08/26/2024] [Indexed: 09/16/2024]
Abstract
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.
Collapse
Affiliation(s)
- Bohan Shi
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| | - Wanbiao Lin
- Shenzhen Research Institute, Nankai University, Shenzhen 518081, China
| | - Wenlan Ouyang
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| | - Chenyu Shen
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| | - Siyang Sun
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| | - Yan Sun
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| | - Lei Sun
- Institute of Robotics & Automatic Information System, Nankai University, Tianjin 300350, China
| |
Collapse
|
2
|
Li Y, Zhao X, Schwertfeger S. Detection and Utilization of Reflections in LiDAR Scans through Plane Optimization and Plane SLAM. SENSORS (BASEL, SWITZERLAND) 2024; 24:4794. [PMID: 39123841 PMCID: PMC11314935 DOI: 10.3390/s24154794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/19/2024] [Accepted: 07/20/2024] [Indexed: 08/12/2024]
Abstract
In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.
Collapse
Affiliation(s)
| | | | - Sören Schwertfeger
- Key Laboratory of Intelligent Perception and Human-Machine Collaboration, ShanghaiTech University, Ministry of Education, Shanghai 201210, China; (Y.L.); (X.Z.)
| |
Collapse
|
3
|
Ma T, Kong L, Ou Y, Xu S. Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm. SENSORS (BASEL, SWITZERLAND) 2024; 24:4011. [PMID: 38931794 PMCID: PMC11209387 DOI: 10.3390/s24124011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/15/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024]
Abstract
Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 ×103 order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward-reverse-forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having O(1) time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.
Collapse
Affiliation(s)
- Tingchen Ma
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (T.M.); (L.K.)
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lingxin Kong
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (T.M.); (L.K.)
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yongsheng Ou
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Sheng Xu
- Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (T.M.); (L.K.)
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
| |
Collapse
|
4
|
Zhang H, Huo J. Non-local affinity adaptive acceleration propagation network for generating dense depth maps from LiDAR. OPTICS EXPRESS 2023; 31:22012-22029. [PMID: 37381285 DOI: 10.1364/oe.492187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/05/2023] [Indexed: 06/30/2023]
Abstract
Depth completion aims to generate dense depth maps from the sparse depth images generated by LiDAR. In this paper, we propose a non-local affinity adaptive accelerated (NL-3A) propagation network for depth completion to solve the mixing depth problem of different objects on the depth boundary. In the network, we design the NL-3A prediction layer to predict the initial dense depth maps and their reliability, non-local neighbors and affinities of each pixel, and learnable normalization factors. Compared with the traditional fixed-neighbor affinity refinement scheme, the non-local neighbors predicted by the network can overcome the propagation error problem of mixed depth objects. Subsequently, we combine the learnable normalized propagation of non-local neighbor affinity with pixel depth reliability in the NL-3A propagation layer, so that it can adaptively adjust the propagation weight of each neighbor during the propagation process, which enhances the robustness of the network. Finally, we design an accelerated propagation model. This model enables parallel propagation of all neighbor affinities and improves the efficiency of refining dense depth maps. Experiments on KITTI depth completion and NYU Depth V2 datasets show that our network is superior to most algorithms in terms of accuracy and efficiency of depth completion. In particular, we predict and reconstruct more smoothly and consistently at the pixel edges of different objects.
Collapse
|
5
|
A Real-Time Monocular Visual SLAM Based on the Bundle Adjustment with Adaptive Robust Kernel. J INTELL ROBOT SYST 2023. [DOI: 10.1007/s10846-023-01817-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
|
6
|
Zhou L, Huang G, Mao Y, Yu J, Wang S, Kaess M. $\mathcal {PLC}$-LiSLAM: LiDAR SLAM With Planes, Lines, and Cylinders. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3180116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | | | - Jincheng Yu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Shengze Wang
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Michael Kaess
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| |
Collapse
|
7
|
Yuan C, Xu W, Liu X, Hong X, Zhang F. Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3187250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Chongjian Yuan
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong
| | - Wei Xu
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong
| | - Xiyuan Liu
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong
| | - Xiaoping Hong
- School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China
| | - Fu Zhang
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong
| |
Collapse
|
8
|
A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs. REMOTE SENSING 2022. [DOI: 10.3390/rs14071741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions caused by LiDAR ego motion. To address the above issues, this paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system. A spherical projection-based feature extraction module is utilized to process the raw point cloud generated by various LiDARs, hence avoiding the time-consuming adaptation of various irregular scan patterns. The extracted features are grouped into higher-level clusters to filter out smaller objects and reduce false matching during feature association. Furthermore, bundle adjustment is adopted to jointly estimate the poses and velocities for multiple scans, effectively improving the velocity estimation accuracy and compensating for point cloud distortions. Experiments on publicly available datasets demonstrate the superiority of VLOM over other state-of-the-art LiDAR-based SLAM systems in terms of accuracy and robustness. Additionally, the satisfactory performance of VLOM on RS-LiDAR-M1, a newly released solid-state LiDAR, shows its applicability to a wide range of LiDARs.
Collapse
|
9
|
Bai C, Xiao T, Chen Y, Wang H, Zhang F, Gao X. Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3152830] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Chunge Bai
- Department of Electronic Information and Engineering, Tsinghua University, Beijing, China
| | - Tao Xiao
- Idriver+ Technologies Company Ltd., Beijing, China
| | - Yajie Chen
- Idriver+ Technologies Company Ltd., Beijing, China
| | - Haoqian Wang
- Department of Electronic Information and Engineering, Tsinghua University, Beijing, China
| | - Fang Zhang
- Idriver+ Technologies Company Ltd., Beijing, China
| | - Xiang Gao
- Idriver+ Technologies Company Ltd., Beijing, China
| |
Collapse
|
10
|
Xue G, Wei J, Li R, Cheng J. LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine. SENSORS 2022; 22:s22020520. [PMID: 35062481 PMCID: PMC8778426 DOI: 10.3390/s22020520] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 11/17/2022]
Abstract
Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.
Collapse
Affiliation(s)
- Guanghui Xue
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China; (G.X.); (J.W.); (R.L.)
- Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing 100083, China
| | - Jinbo Wei
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China; (G.X.); (J.W.); (R.L.)
| | - Ruixue Li
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China; (G.X.); (J.W.); (R.L.)
| | - Jian Cheng
- Research Institute of Mine Big Data, China Coal Research Institute, Beijing 100013, China
- Correspondence:
| |
Collapse
|
11
|
Reinke A, Palieri M, Morrell B, Chang Y, Ebadi K, Carlone L, Agha-Mohammadi AA. LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3181357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Andrzej Reinke
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Matteo Palieri
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Benjamin Morrell
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Yun Chang
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamak Ebadi
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Luca Carlone
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | |
Collapse
|
12
|
|
13
|
Li F, Liu S, Zhao X, Zhang L. Real-Time 2-D Lidar Odometry Based on ICP. SENSORS 2021; 21:s21217162. [PMID: 34770487 PMCID: PMC8587105 DOI: 10.3390/s21217162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/20/2021] [Accepted: 10/25/2021] [Indexed: 11/20/2022]
Abstract
This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.
Collapse
|
14
|
Zhou L, Koppel D, Kaess M. LiDAR SLAM With Plane Adjustment for Indoor Environment. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3092274] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
15
|
Lin J, Zheng C, Xu W, Zhang F. R $^2$ LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3095515] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
16
|
Yuan C, Liu X, Hong X, Zhang F. Pixel-Level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3098923] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
17
|
Koide K, Yokozuka M, Oishi S, Banno A. Globally Consistent 3D LiDAR Mapping With GPU-Accelerated GICP Matching Cost Factors. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3113043] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|