Ground Robot Relocation Method Based on UAV Point Cloud Map

doi:10.16182/j.issn1004731x.joss.23-0751

Abstract

Abstract:

In response to the challenge of relocalization in air-ground collaborative systems without the support of Global Navigation Satellite System (GNSS), and the associated issues of insufficient accuracy, a coarse-to-fine relocalization algorithm based on a three-dimensional point cloud map is proposed. The algorithm eliminates the influence of invalid point clouds from the sky and ground through index filtering, performs coarse localization by extracting global features from the point cloud and applying truncated least squares estimation, and then employs voxel-based iterative closest point (ICP) for precise optimization to obtain the more accurate localization results. A ground robot localization and autonomous navigation framework is constructed based on an aerial global map and the feasibility of the framework is validated through experiments on a simulation platform, while the real-time and accuracy of the ground robot relocalization algorithm is verified.

Key words: ground robot, relocalization, UAV, global point cloud map, autonomous navigation

CLC Number:

TP242

Huang Hongzhi, Yan Kai, Liu Changfeng, Wang Jianwen, Luo Bin. Ground Robot Relocation Method Based on UAV Point Cloud Map[J]. Journal of System Simulation, 2024, 36(10): 2444-2454.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 1

Fig. 10

Table 2

Table 3

Fig. 11

References 29

1	Butzke J, Gochev K, Holden B, et al. Planning for a Ground-air Robotic System with Collaborative Localization[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2016: 284-291.
2	朱新平, 瞿菁菁, 徐川. 森林火灾救援空地一体化指挥体系设计[J]. 消防科学与技术, 2022, 41(9): 1287-1290.
	Zhu Xinping, Qu Jingjing, Xu Chuan. Air-ground Integrated Command System Design for Forest Fire Rescue[J]. Fire Science and Technology, 2022, 41(9): 1287-1290.
3	Mehmood Saqib, Ahmed Shakeel, Anders Schmidt Kristensen, et al. Multi Criteria Decision Analysis (MCDA) of Unmanned Aerial Vehicles (UAVs) as a Part of Standard Response to Emergencies[C]//4th International Conference on Green Computing and Engineering Technologies. Esbjerg: Gyancity International Publishers, 2018: 116.
4	Batzdorfer S, Bobbe M, Becker M, et al. Multisensor Equipped UAV/UGV for Automated Exploration[C]//2017 International Conference on Unmanned Aerial Vehicles. Hannover: ISPRS, 2017: 33-40.
5	Mueggler Elias, Faessler Matthias, Fontana Flavio, et al. Aerial-guided Navigation of a Ground Robot Among Movable Obstacles[C]//2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014). Piscataway: IEEE, 2014: 1-8.
6	El Houssein Chouaib Harik, Guérin François, Guinand Frédéric, et al. UAV-UGV Cooperation for Objects Transportation in an Industrial Area[C]//2015 IEEE International Conference on Industrial Technology (ICIT). Piscataway: IEEE, 2015: 547-552.
7	Heppner Georg, Roennau Arne, Dillman Ruediger. Enhancing Sensor Capabilities of Walking Robots Through Cooperative Exploration with Aerial Robots[J]. Journal of Automation, Mobile Robotics and Intelligent Systems, 2013, 7(2): 5-11.
8	马跃龙, 曹雪峰, 陈丁, 等. 一种基于点云地图的机器人室内实时重定位方法[J]. 系统仿真学报, 2017, 29(增1): 15-23, 29.
	Ma Yuelong, Cao Xuefeng, Chen Ding, et al. A Method for Real-time Relocalization of Indoor Robot with Point Cloud Map[J]. Journal of System Simulation, 2017, 29(S1): 15-23, 29.
9	He Li, Wang Xiaolong, Zhang Hong. M2DP: A Novel 3D Point Cloud Descriptor and Its Application in Loop Closure Detection[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2016: 231-237.
10	Uy M A, Lee G H. PointNetVLAD: Deep Point Cloud Based Retrieval for Large-scale Place Recognition[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 4470-4479.
11	Kim Giseop, Kim Ayoung. Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2018: 4802-4809.
12	Bosse M, Zlot R. Place Recognition Using Keypoint Voting in Large 3D Lidar Datasets[C]//Proceedings of 2013 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2013: 2677-2684.
13	Gawel Abel, Cieslewski Titus, Dubé Renaud, et al. Structure-based Vision-laser Matching[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2016: 182-188.
14	Segal A V, Haehnel D, Thrun S. Generalized-ICP[EB/OL]. [2023-06-12]. .
15	Rusu R B, Cousins S. 3D Is Here: Point Cloud Library (PCL)[C]//2011 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2011: 1-4.
16	Yang Heng, Shi Jingnan, Carlone L. TEASER: Fast and Certifiable Point Cloud Registration[J]. IEEE Transactions on Robotics, 2021, 37(2): 314-333.
17	Zhou Qianyi, Park J, Koltun V. Fast Global Registration[C]//Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 766-782.
18	Yang Heng, Carlone L. A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates[EB/OL]. (2019-06-30) [2023-06-12]. .
19	Yang Heng, Antonante P, Tzoumas V, et al. Graduated Non-convexity for Robust Spatial Perception: From Non-minimal Solvers to Global Outlier Rejection[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 1127-1134.
20	Perera S, Barnes N. Maximal Cliques Based Rigid Body Motion Segmentation with a RGB-D Camera[C]//Computer Vision-ACCV 2012. Berlin: Springer Berlin Heidelberg, 2013: 120-133.
21	Koide Kenji, Yokozuka Masashi, Oishi Shuji, et al. Voxelized GICP for Fast and Accurate 3D Point Cloud Registration[C]//2021 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2021: 11054-11059.
22	Bai Chunge, Xiao Tao, Chen Yajie, et al. Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry Using Parallel Sparse Incremental Voxels[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 4861-4868.
23	岳伟韬, 苏婧, 谷志珉, 等. 占据栅格地图的最佳栅格大小与地图精度[J]. 机器人, 2020, 42(2): 199-206.
	Yue Weitao, Su Jing, Gu Zhimin, et al. Best Grid Size of the Occupancy Grid Map and Its Accuracy[J]. Robot, 2020, 42(2): 199-206.
24	Hart P E, Nilsson N J, Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107.
25	Cao Chao, Zhu Hongbiao, Yang Fan, et al. Autonomous Exploration Development Environment and the Planning Algorithms[C]//2022 International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2022: 8921-8928.
26	Shah S, Dey D, Lovett C, et al. AirSim: High-fidelity Visual and Physical Simulation for Autonomous Vehicles[C]//Field and Service Robotics. Cham: Springer International Publishing, 2018: 621-635.
27	Hartley R, Trumpf J, Dai Yuchao, et al. Rotation Averaging[J]. International Journal of Computer Vision, 2013, 103(3): 267-305.
28	Baek Jieun, Park Junhyeok, Cho Seongjun, et al. 3D Global Localization in the Underground Mine Environment Using Mobile LiDAR Mapping and Point Cloud Registration[J]. Sensors, 2022, 22(8): 2873.
29	Zha Yongjian, Chen Ming, Lu Shenglian. Research on Point Cloud Registration of Industrial Parts Based on FGR-ICP Algorithm[J]. Journal of Physics: Conference Series, 2021, 1941(1): 012014.

场景	数据组号	局部点云组数量	重定位成功数量	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
工厂	1	73	70	95.89	0.113	0.009	14.377
	2	58	56	96.55	0.144	0.010	14.693
	3	103	100	97.09	0.112	0.011	14.587
	4	63	60	95.24	0.113	0.009	14.238
城市	1	94	93	98.94	0.109	0.008	17.753
	2	130	127	97.69	0.132	0.012	17.682
	3	89	85	95.51	0.125	0.010	17.365
	4	168	162	96.43	0.146	0.013	17.587

算法	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
本文算法	97.08	0.112	0.011 1	14.587
FPFH-RANSAC粗匹配+ICP细匹配	36.89	0.263	0.012 8	20.109
FGR粗匹配+ICP细匹配	90.29	0.108	0.009 8	32.869

高斯噪声方差/m	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
0	97.08	0.112	0.011 1	14.587
0.05	97.08	0.115	0.010 9	14.786
0.10	96.11	0.127	0.013 6	17.830
0.15	92.23	0.198	0.013 4	22.634
0.20	88.34	0.277	0.016 4	28.611

[1]	Li Erchao, Zhang Shenghui. UAV Online Track Planning Based on DMOEA-APTC Algorithm [J]. Journal of System Simulation, 2024, 36(9): 2086-2099.
[2]	Liu Bin, Lan Ying, Huang Wentao, Fan Qinqin. UAV Dynamic Path Planning Algorithm Combined with Dynamic Window Approach [J]. Journal of System Simulation, 2024, 36(8): 1843-1853.
[3]	Huang Zhifeng, Liu Yuanhua. UAV Path Planning Based on Improved Harris Hawk Algorithm and B-spline Curve [J]. Journal of System Simulation, 2024, 36(7): 1509-1524.
[4]	Wang Tao, Ji Xiaodong. Optimal Trajectory of Full-duplex UAV Relaying over Hybrid Probability Channels [J]. Journal of System Simulation, 2024, 36(6): 1369-1377.
[5]	Zhu Jingyu, Zhang Hongli, Kuang Minchi, Shi Heng, Zhu Jihong, Qiao zhi, Zhou Wenqing. Curriculum Learning-based Simulation of UAV Air Combat Under Sparse Rewards [J]. Journal of System Simulation, 2024, 36(6): 1452-1467.
[6]	Xiao Peng, Xie Feng, Ni Haihong, Zhang Min, Tang Zhili, Li Ni. Research on Collaborative Optimization Method of Multi-UAV Task Allocation and Path Planning [J]. Journal of System Simulation, 2024, 36(5): 1141-1151.
[7]	Gao Chao, Huang Zheng, Zhao Xuan, Wang Hongxing, Long Tao. Collaborative Navigation Method for 5G Cluster UAV Based on Configuration Optimization [J]. Journal of System Simulation, 2024, 36(4): 981-990.
[8]	Yu Xiang, Deng Qianrui, Duan Sirui, Jiang Chen. A Multi-UAV Collaborative Priority Coverage Search Algorithm [J]. Journal of System Simulation, 2024, 36(4): 991-1000.
[9]	Gui Xueqi, Li Chuntao. UAV Swarm Obstacle Avoidance Algorithm Based on Visual Field and Velocity Guidance [J]. Journal of System Simulation, 2024, 36(3): 545-554.
[10]	Li Gaoyang, Li Xiangfeng, Zhao Kang, Jin Yuchao, Yi Zhidong, Zuo Dunwen. Three-Dimensional Path Planning of UAV Based on All Particles Driving Wild Horse Optimizer Algorithm [J]. Journal of System Simulation, 2024, 36(3): 595-607.
[11]	Lu Yuanjie, Long Shanshan, Zhao Hang, Feng Guoxu, Zhao Xiaojia. Effectiveness Evaluation of Heterogeneous UAV Swarms Based on a Hybrid Model [J]. Journal of System Simulation, 2024, 36(3): 700-712.
[12]	Yang Zhe, Cui Yinghan, Guo Lingxi, Li Jiaxin, Wu Xusheng. Search Technology for Aircraft Debris Integrating Data Augmentation and Deep Learning Algorithm [J]. Journal of System Simulation, 2024, 36(10): 2238-2245.
[13]	Cheng Jie, Zheng Yuan, Li Chenglong, Jiang Bo. Multi-UAV Collaborative Trajectory Planning Algorithm for Urban Ultra-low-altitude Air Transportation Scenario [J]. Journal of System Simulation, 2024, 36(1): 50-66.
[14]	Jiao Songming, Shou Yunfeng, Bai Jianpeng, Wang Zhu. Research on Hierarchical Motion Planning Method for UAV Substation Inspection [J]. Journal of System Simulation, 2023, 35(9): 1975-1984.
[15]	Shuangcheng Niu, Yuqiang Jin, Kunhu Kou. Research on Collaborative Task Allocation Method of Multiple UAVs Based on Blockchain [J]. Journal of System Simulation, 2023, 35(5): 949-956.