Swinging Single-Layer LiDAR Based Dense Point Cloud MapReconstruction System for Large-Scale Scenes
QIAN Chaojie1, YANG Ming2,3, QI Mingxu2,3, WANG Chunxiang2,3, WANG Bing2,3
1. Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
3. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
Abstract：When creating dense point cloud maps for large-scale scenes, it's difficult for current 3D measurement systems to balance their measurement range and point cloud density. Therefore, a swinging single-layer LiDAR based dense point cloud map reconstruction system for large-scale scenes is designed. Firstly, the stable and accurate omnidirectional swing of a large LiDAR is realized. Then, the point cloud concatenation method at a single measuring point and the registration method at multiple measuring points are given. Finally, a density analysis method for projected 3D point cloud is proposed, and the simulation results are compared and evaluated. Experimental results show that the effective measurement distance of the system exceeds 75 m, the measurement range covers ±45° pitching angle, the point cloud spacing is less than 20 cm, and the point cloud distribution is uniform. Meanwhile, the view field and point cloud distribution of the device can be adjusted, and a larger scene can be reconstructed by point cloud registration at multiple measuring points.
[1] Carlevaris-Bianco N, Ushani A K, Eustice R M. University of Michigan North Campus long-term vision and LiDAR dataset[J]. International Journal of Robotics Research, 2016, 35(9):1023-1035. [2] Kong D M, Xu L J, Li X L, et al. K-plane-based classification of airborne LiDAR data for accurate building roof measurement[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(5):1200-1214. [3] Li L, Yang M, Guo L D, et al. Hierarchical neighborhood based precise localization for intelligent vehicles in urban environments[J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(3):220-229. [4] Cadena C, Carlone L, Carrillo H, et al. Past, present, and future of simultaneous localization and mapping:Toward the robust-perception age[J]. IEEE Transactions on Robotics, 2016, 32(6):1309-1332. [5] 于宁波,王石荣,徐昌.一种基于RGB-D的移动机器人未知室内环境自主探索与地图构建方法[J].机器人,2017,39(6):860-871. Yu N B, Wang S R, Xu C. RGB-D based autonomous exploration and mapping of a mobile robot in unknown indoor environment[J]. Robot, 2017, 39(6):860-871. [6] Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//IEEE and ACM International Symposium on Mixed and Augmented Reality. Piscataway, USA:IEEE, 2007:225-234. [7] Agrawal P, Iqbal A, Russell B, et al. PCE-SLAM:A real-time simultaneous localization and mapping using LiDAR data[C]//IEEE Intelligent Vehicles Symposium. Piscataway, USA:IEEE, 2017:1752-1757. [8] 王任栋,徐友春,齐尧,等.一种鲁棒的城市复杂动态场景点云配准方法[J].机器人,2018,40(3):257-265. Wang R D, Xu Y C, Qi Y, et al. A robust point cloud registration method in urban dynamic environment[J]. Robot, 2018, 40(3):257-265. [9] Zhang J, Singh S. Low-drift and real-time lidar odometry and mapping[J]. Autonomous Robots, 2017, 41(2):401-416. [10] Nagatani K, Tokunaga N, Okada Y, et al. Continuous acquisition of three-dimensional environment information for tracked vehicles on uneven terrain[C]//IEEE InternationalWorkshop on Safety, Security and Rescue Robotics. Piscataway, USA:IEEE, 2008:25-30. [11] Yoshida T, Irie K, Koyanagi E, et al. 3D laser scanner with gazing ability[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2011:3098-3103. [12] 谷晓杰,卜春光,陈成,等.三维激光测距系统设计与标定方法研究[J].沈阳理工大学学报,2014,33(5):10-14. Gu X J, Bu C G, Chen C, et al. Research on the design and calibration method of 3D laser ranging system[J]. Journal of Shenyang Ligong University, 2014, 33(5):10-14. [13] Ohno K, Tadokoro S, Nagatani K, et al. Trials of 3-D map construction using the tele-operated tracked vehicle Kenaf at Disaster City[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2010:2864-2870. [14] Magnusson M. The three-dimensional normal-distributions transform:An efficient representation for registration, surface analysis, and loop detection[D]. Ö rebro, Sweden:Ö rebro University, 2009. [15] Desai A, Huber D. Objective evaluation of scanning ladar configurations for mobile robots[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2009:2182-2189. [16] Sarangi S. Surface reconstruction from unorganized point cloud data using incremental Delaunay triangulation[M]. Buffalo, USA:State University of New York at Buffalo, 2007.
