[1]殷江、林建德、孔令华、邹诚、游通飞、易定容.基于激光雷达的移动机器人三维建图与定位[J].福建工程学院学报,2020,18(04):370-374.[doi:10.3969/j.issn.1672-4348.2020.04.012]
　YIN Jiang,LIN Jiande,KONG Linghua,et al.3D mapping and positioning of mobile robots based on lidar[J].Journal of FuJian University of Technology,2020,18(04):370-374.[doi:10.3969/j.issn.1672-4348.2020.04.012]
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基于激光雷达的移动机器人三维建图与定位()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第18卷
期数:
2020年04期
页码:
370-374
栏目:
出版日期:
2020-08-25

文章信息/Info

Title:
3D mapping and positioning of mobile robots based on lidar
作者:
殷江、林建德、孔令华、邹诚、游通飞、易定容
福建工程学院设计学院
Author(s):
YIN Jiang;  LIN Jiande;  KONG Linghua;  ZOU Cheng;  YOU Tongfei;  YI Dingrong
School of Design, Fujian University of Engineering
关键词:
移动机器人;  激光雷达;  回环检测;  累计漂移误差;  点云片段匹配
Keywords:
mobile robot;  lidar;  loop detection;  accumulated drift error;  point cloud segment matching
分类号:
TP242
DOI:
10.3969/j.issn.1672-4348.2020.04.012
文献标志码:
A
摘要:
针对室外环境建图与定位缺乏有效的回环检测导致累计漂移误差以及点云地图形式不够紧凑，提出基于Livox(览沃)激光雷达采集数据模块，使用三维点云片段匹配方法消除室外建图出现的误差。首先，对激光雷达采集的三维点云数据进行采样和体素滤波完成数据预处理?然后，使loam(lidarodometryandmappinginreal-time)算法作为前端，采用ICP算法实现快速有效的帧间匹配?最后，结合三维点云片段匹配与GSTAM优化位姿累计误差获得全局一致的的轨迹，并将点云地图优化成为立体占用地图输出。通过将点云片段匹配方法作为回环检测在实际室外环境进行三维激光建图实验证明，该方法能够解决实际室外环境建图中存在的建图不精准的问题。
Abstract:
In view of the lack of effective loop detection for 3D laser mapping and positioning in outdoor environment, which leads to cumulative drift error and insufficient compactness of the point cloud map form, a Livox-based lidar data acquisition module was proposed, using the 3D point cloud segment matching method to eliminate errors in outdoor mapping process. First, perform down-sampling and voxel filtering on the three-dimensional point cloud data collected by livox lidar to complete data preprocessing. Then, use the LOAM(lidar odometry and mapping in real-time) algorithm as the front end, and use the ICP algorithm to achieve fast and effective inter-frame matching. Finally, combine the three-dimensional point cloud segment matching and the GSTAM optimized pose cumulative error to obtain a globally consistent trajectory, and optimize the point cloud map into a stereo occupancy map output. By using the point cloud segment matching method as loop detection, the 3D laser mapping experiment in the actual outdoor environment proves that the method can solve the problem of inaccurate mapping in the actual outdoor environment.

参考文献/References:

[1] NELSON E. Berkerley localization and mapping[DB/OL]. https:∥github.com/eriknelson/blam.[2] HESS W,KOHLER D,RAPP H,et al. Real-time loop closure in 2D LIDAR SLAM[C]∥2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016: 1271-1278.[3] ZHANG J, SINGH S. Low-drift and real-time lidar odometry and mapping[J]. Autonomous Robots, 2017, 41(2): 401-416.[4] DUBE R, DUGAS D, STUMM E, et al. SegMatch: Segment based place recognition in 3D point clouds[C]∥2017 IEEE International Conference on Robotics and Automation (ICRA). Singapore, 2017:5266-5272[5] DELLAERT F. Factor graphs and GTSAM: A hands-on introduction: GT-ＲIM-CP＆Ｒ-2012-02[R]. Altanlanta: Georgia Institute of Technology, 2012: 1-26.[6] KAESS M, JOHANNSSON H, ROBERTS R, et al. iSAM2: Incremental smoothing and mapping using the Bayes tree[J]. The International Journal of Robotics Research, 2012, 31(2): 216-235.[7] DOUILLARD B, UNDERWOOD J, KUNTZ N, et al. On the segmentation of 3D LIDAR point clouds[C]∥2011 IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011: 2798-2805.[8] WOHLKINGER W, VINCZE M. Ensemble of shape functions for 3D object classification[C]∥2011 IEEE International Conference on Robotics and Biomimetics. Karon Beach, Phuket, Thailand: IEEE, 2011: 2987-2992.[9] FISCHLER M, BOLLES R. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. mmunications of the ACM, 981,24(6): 381-395.[10] HERBERT M, CAILLAS C, KROTKOV E, et al. Terrain mapping for a roving planetary explorer[C]∥Proceedings, 1989 International Conference on Robotics and Automation. Scottsdale, AZ, USA: IEEE, 1989: 997-1002.[11] FIORAIO N, KONOLIGE K. Realtime visual and point cloud SLAM[J]. Computer, 2011: 1-3.[12] MARTIN C, THRUN S. Real-time acquisition of compact volumetric 3D maps with mobile robots[C]∥Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292). Washington, DC: IEEE, 2002: 311-316.[13] LI X, GUO X, WANG H, et al. Harmonic volumetric mapping for solid modeling applications[C]∥Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling-SPM ’07. New York: ACM Press, 2007: 109-120.[14] PAILL G, POULIN P. As-conformal-as-possible discrete volumetric mapping[J]. Computers & Graphics, 2012, 36(5): 427-433. [15] WEINMANN M, JUTZI B, MALLET C. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, II-3: 181-188.[16] 危双丰, 庞帆, 刘振彬, 等. 基于激光雷达的同时定位与地图构建方法综述[J]. 计算机应用研究, 2020, 37(2): 327-332.[17] 李帅鑫, 李广云, 王力, 等. Lidar/IMU紧耦合的实时定位方法[J]. 自动化学报, 2020, 46(x)：1-13.

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更新日期/Last Update: 2020-08-25