基于VSLAM的自主移动机器人三维同时定位与地图构建

doi:10.11772/j.issn.1001-9081.2017.10.2884

计算机应用 ›› 2017, Vol. 37 ›› Issue (10): 2884-2887.DOI: 10.11772/j.issn.1001-9081.2017.10.2884

• 计算机视觉与虚拟现实 • 上一篇下一篇

基于VSLAM的自主移动机器人三维同时定位与地图构建

林辉灿¹, 吕强¹, 王国胜¹, 张洋¹, 梁冰²

1. 装甲兵工程学院控制工程系, 北京 100072;
2. 江西理工大学信息工程学院, 江西赣州 341000

收稿日期:2017-04-05 修回日期:2017-06-18 出版日期:2017-10-10 发布日期:2017-10-16
通讯作者: 吕强(1962-),男,北京人,教授,博士,主要研究方向:机器人控制、机器视觉,E-mail:rokyou@live.cn
作者简介:林辉灿(1989-),男,福建厦门人,博士研究生,主要研究方向:VSLAM、自主移动机器人;吕强(1962-),男,北京人,教授,博士,主要研究方向:机器人控制、机器视觉;王国胜(1975-),男,北京人,副教授,博士,主要研究方向:非线性控制、计算机视觉;张洋(1987-),男,吉林四平人,博士研究生,主要研究方向:计算机视觉、SLAM;梁冰(1975-),女,副教授,博士,主要研究方向:鲁棒容错控制.
基金资助:
国家自然科学基金资助项目（61663014）。

3D simultaneous localization and mapping for mobile robot based on VSLAM

LIN Huican¹, LYU Qiang¹, WANG Guosheng¹, ZHANG Yang¹, LIANG Bing²

1. Department of Control Engineering, Academy of Armored Force Engineering, Beijing 100072, China;
2. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China

Received:2017-04-05 Revised:2017-06-18 Online:2017-10-10 Published:2017-10-16
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61663014).

摘要/Abstract

摘要： 移动机器人在探索未知环境且没有外部参考系统的情况下，面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题，提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先，根据关键帧的位姿和深度数据，构建图像对应场景的点云地图；然后利用八叉树地图技术进行处理，构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验，实验结果表明，所提算法具有较高的有效性、精度和鲁棒性。最后，搭建了自主移动机器人，将改进的VSLAM系统应用到移动机器人中，能够实时地完成自主避障和三维地图构建，解决稀疏地图无法用于避障和导航的问题。

关键词: 计算机视觉, 同时定位与地图构建, 自主移动机器人, 八叉树地图, 避障

Abstract: The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.

Key words: computer vision, Simultaneous Localization And Mapping (SLAM), autonomous mobile robot, octree map, obstacle avoidance

中图分类号:

TP242.6

林辉灿, 吕强, 王国胜, 张洋, 梁冰. 基于VSLAM的自主移动机器人三维同时定位与地图构建[J]. 计算机应用, 2017, 37(10): 2884-2887.

LIN Huican, LYU Qiang, WANG Guosheng, ZHANG Yang, LIANG Bing. 3D simultaneous localization and mapping for mobile robot based on VSLAM[J]. Journal of Computer Applications, 2017, 37(10): 2884-2887.

参考文献

[1] FUENTES-PACHECO J, RUIZ-ASCENCIO J, RENDON-MANCHA J M. Visual simultaneous localization and mapping: a survey[J]. Artificial Intelligence Review, 2015, 43(1): 55-81.
[2] KLEIN G, MURRAY D. Parallel tracking and mapping for small AR workspaces[C]//ISMAR 2007: Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. Piscataway, NJ: IEEE, 2007: 225-234.
[3] KERL C, STURM J, CREMERS D. Robust odometry estimation for RGB-D cameras[C]//Proceedings of the 2013 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2013: 3748-3754.
[4] KERL C, STURM J, CREMERS D. Dense visual SLAM for RGB-D cameras[C]//Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ: IEEE, 2013: 2100-2106.
[5] HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments[C]//Proceedings of the 12th International Symposium on Experimental robotics. Berlin: Springer, 2014: 477-491.
[6] ENDRES F, HESS J, STURM J, et al. 3-D mapping with an RGB-D camera[J]. IEEE Transactions on Robotics, 2014, 30(1): 177-187.
[7] GAO X, ZHANG T. Robust RGB-D simultaneous localization and mapping using planar point features[J]. Robotics and Autonomous Systems, 2015, 72: 1-14.
[8] WHELAN T, SALAS-MORENO R F, GLOCKER B, et al. ElasticFusion: real-time dense SLAM and light source estimation[J]. The International Journal of Robotics Research, 2016, 35(14): 1697-1716.
[9] MUR-ARTAL R, MONTIEL J M M, TARDÓS J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163.
[10] 侯荣波, 魏武, 黄婷, 等. 基于ORB-SLAM的室内机器人定位和三维稠密地图构建[J]. 计算机应用, 2017, 37(5): 1439-1444. (HOU R B, WEI W, HUANG T, et al. Indoor robot localization and 3D dense mapping based on ORB-SLAM[J]. Journal of Computer Applications, 2017, 37(5): 1439-1444.)
[11] HORNUNG A, WURM K M, BENNEWITZ M, et al. OctoMap: an efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots, 2013, 34(3): 189-206.
[12] MEIER L, TANSKANED P, HENG L, et al. PIXHAWK: a micro aerial vehicle design for autonomous flight using onboard computer vision[J]. Autonomous Robots, 2012, 33(1/2): 21-39.
[13] MEIER L, HONEGGER D, POLLEFEYS M. PX4: a node-based multithreaded open source robotics framework for deeply embedded platforms[C]//Proceedings of the 2015 IEEE International Conference on Robotics and Automation. Piscataway, NJ: IEEE, 2015: 6235-6240.
[14] MARTINEZ A, FERNANDEZ E. Learning ROS for Robotics Programming[M]. Birmingham: Packt Publishing, 2013: 45-48.
[15] STURM J, ENGELHARD N, ENDRES F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]//Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ: IEEE, 2012: 573-580.

基于VSLAM的自主移动机器人三维同时定位与地图构建

3D simultaneous localization and mapping for mobile robot based on VSLAM

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