基于全局特征点匹配的全局定位方法

doi:10.3778/j.issn.1002-8331.2108-0304

摘要/Abstract

摘要： 针对传统全局定位方法存在对传感器要求多、计算量大的问题，提出了一种基于全局特征点匹配的移动机器人定位方法。该方法采用普通2D雷达作为传感器，在机器人建立全局地图的过程中同步地提取全局特征点，在全局定位算法中，通过建立局部地图和提取局部地图特征点，实时将局部地图特征点和全局地图特征点进行匹配后求解全局位姿。在两个数据集上的测试，结果优于蒙特卡罗自适应定位（adaptive Monte Carlo localization，AMCL）和Cartographer的全局定位效果，运算速度更快。结果表明，与已有的方法相比，该全局定位方法能够更快地完成全局定位和有效减少计算资源的消耗。

关键词: 全局定位, 全局特征点, 匹配, 移动机器人

Abstract: To solve the problem of high demand of sensor and large amount of calculation in traditional global localization method, this paper proposes a novel localization algorithm for mobile robots based on global feature point matching using 2D LiDAR sensors. The global feature points are synchronously extracted while global map building. Within the global localization algorithm, local maps are established and local feature points are further extracted, then the global pose can be obtained by a real-time comparison and matching between local and global feature points. The proposed method performs better than the AMCL（adaptive Monte Carlo localization） and Cartographer methods in positioning accuracy and calculating speed on two test sets. The results shows that, this method can effectively make the robot localization faster, as well as reduce the consumption of computing resources.

Key words: global localization, global feature points, matching, mobile robots

彭泽鑫, 曾碧, 刘建圻. 基于全局特征点匹配的全局定位方法[J]. 计算机工程与应用, 2023, 59(3): 264-275.

PENG Zexin, ZENG Bi, LIU Jianqi. Global Localization Method Based on Global Feature Point Matching[J]. Computer Engineering and Applications, 2023, 59(3): 264-275.

参考文献

[1] DURRANT-WHYTE H，BAILEY T.Simultaneous localization and mapping：part I[J].IEEE Robotics & Automation Magazine，2006，13（2）：99-110.
[2] BAILEY T，DURRANT-WHYTE H.Simultaneous localization and mapping（SLAM）：part II[J].IEEE Robotics & Automation Magazine，2006，13（3）：108-117.
[3] THRUN S，BURGARD W，FOX D.Probabilistic robotics[M].[S.l.]：MIT Press，2006.
[4] AZZI C.Efficient image-based localization using context[D].University of Waterloo，2015.
[5] SINGH G，KOSECKA J.Visual loop closing using gist descriptors in manhattan world[C]//ICRA Omnidirectional Vision Workshop，2010：4042-4047.
[6] CUMMINS M，NEWMAN P.FAB-MAP：probabilistic localization and mapping in the space of appearance[J].The International Journal of Robotics Research，2008，27（6）：647-665.
[7] LABBE M，MICHAUD F.Online global loop closure detection for large-scale multi-session graph-based SLAM[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems，2014：2661-2666.
[8] CAMPOS C，ELVIRA R，RODRíGUEZ J J G，et al.ORB-SLAM3：an accurate open-source library for visual，visual-inertial，and multimap SLAM[J].IEEE Transactions on Robotics，2021，37（6）：1874-1890.
[9] KENDALL A，GRIMES M，CIPOLLA R.Posenet：a convolutional network for real-time 6-dof camera relocalization[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：2938-2946.
[10] LI Y，CHEN C P，MAITLO N，et al.Deep neural network-based loop detection for visual simultaneous localization and mapping featuring both points and lines[J].Advanced Intelligent Systems，2020，2（1）：1900107.
[11] THRUN S，FOX D，BURGARD W，et al.Robust Monte Carlo localization for mobile robots[J].Artificial Intelligence，2001，128（1/2）：99-141.
[12] 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（ICRA），2016：1271-1278.
[13] WANG X，ZHANG H，PENG G.3-DOF point cloud registration using congruent triangles[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems（IROS），2015：1943-1948.
[14] LIU Z，ZHOU S，SUO C，et al.Lpd-net：3d point cloud learning for large-scale place recognition and environment analysis[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：2831-2840.
[15] KONG X，YANG X，ZHAI G，et al.Semantic graph based place recognition for 3d point clouds[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems（IROS），2020：8216-8223.
[16] XIANG H，SHI W，FAN W，et al.FastLCD：a fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping[J].International Journal of Applied Earth Observation and Geoinformation，2021，102：102430.
[17] ZHANG J，SINGH S.LOAM：lidar odometry and mapping in real-time[C]//Robotics：Science and Systems，2014.
[18] FALLON M，JOHANNSSON H，KAESS M，et al.The mit stata center dataset[J].The International Journal of Robotics Research，2013，32（14）：1695-1699.