基于RGB-D图像的三维同步定位与建图研究

doi:10.16182/j.issn1004731x.joss.201711033

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (11): 2840-2846.doi: 10.16182/j.issn1004731x.joss.201711033

基于RGB-D图像的三维同步定位与建图研究

胡凌燕¹, 曹禄¹, 熊鹏文^1*, 辛勇², 谢泽坤¹

1.南昌大学信息工程学院,南昌 330031;
2.南昌大学理学院,南昌 330031

收稿日期:2016-08-26 发布日期:2020-06-05
作者简介:胡凌燕(1978-), 女, 江西, 博士, 教授, 研究方向为机器人控制算法、网络控制系统; 曹禄(1992-), 男, 江西, 硕士生, 研究方向为移动机器人视觉SLAM;熊鹏文(通信作者1987-),男,江西,博士,讲师,研究方向为机器人传感与控制技术。
基金资助:
国家自然科学基金(61563035, 81501560, 61662044, 61663027)

3D Simultaneous Localization and Mapping Based on RGB-D Images

Hu Lingyan¹, Cao Lu¹, Xiong Pengwen^1*, Xin Yong², Xie Zekun¹

1. School of Information Engineering, Nanchang University, Nanchang 330031, China;
2. School of Sciences, Nanchang University, Nanchang 330031, China

Received:2016-08-26 Published:2020-06-05

摘要/Abstract

摘要： 针对移动机器人三维同步定位与建图过程中机器人位姿误差累积问题,提出了一种位姿全局优化方法提高机器人定位精度和建图质量。该方法在帧到帧配准模型的视觉里程计的基础上,通过基于图像匹配的闭环检测来增加机器人位姿间的约束,在构建位姿图过程中采用局部回环结合随机大回环策略提高位姿优化效率,最后采用g2o (general graph optimization)算法对机器人位姿进行全局优化。此外,提出了一种关键帧选取方法,以减少系统计算资源及内存空间的消耗。实验结果表明,该方法在运动轨迹3.96 m的情况下均方根误差仅为8.7 mm,并能准确构建出室内场景的三维地图。

关键词: 同步定位与建图, 视觉里程计, 闭环检测, 关键帧, 图优化

Abstract: To reduce the accumulated pose error of robots during the three-dimensional simultaneous localization and mapping, a global optimization method is proposed to improve the positioning accuracy and the quality of the map. This method, which is based on the visual odometry of the frame and frame registration model, adds the pose-constraints by closed-loop detection based on image matching. Local loop is combined with random loop to improve the optimization efficiency. The general graph optimization algorithm is used to globally optimize the robot poses. A key-frame selection strategy is also proposed to decrease the consumption of the computing resources and memory footprint. The experiment results show that this method can reduce the root mean square error to only 8.7mm with a 3.96 m path and generate 3D map of indoor scenes accurately.

Key words: SLAM, visual odometry, loop-detection, key-frame, general graph optimization

中图分类号:

TP391.41

胡凌燕, 曹禄, 熊鹏文, 辛勇, 谢泽坤. 基于RGB-D图像的三维同步定位与建图研究[J]. 系统仿真学报, 2017, 29(11): 2840-2846.

Hu Lingyan, Cao Lu, Xiong Pengwen, Xin Yong, Xie Zekun. 3D Simultaneous Localization and Mapping Based on RGB-D Images[J]. Journal of System Simulation, 2017, 29(11): 2840-2846.

参考文献

[1] 刘艳丽. 融合颜色和深度信息的三维同步定位与地图构建研究[D]. 中南大学, 2014.
Liu Yanli.Research on 3D Simultaneous Localization and Mapping Fusing Color and Depth Information[D]. Changsha, China: Central South University, 2014.
[2] 梁明杰, 闵华清, 罗荣华. 基于图优化的同时定位与地图创建综述[J]. 机器人, 2013, 35(4): 500-512.
LIANG Mingjie, MIN Huaqing, LUO Ronghua.A summary of simultaneous location and map creation based on graph optimization[J]. Robot, 2013, 35(4):500-512.
[3] Dryanovski I, Valenti R G, Xiao J Z.Fast visual odometry and mapping from RGB-D data[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2013: 2305-2310.
[4] Schleicher D, Bergasa L M, Ocaña M, et al.Real-Time Stereo Visual SLAM in Large-Scale Environments Based on SIFT Fingerprints[C]//IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007: 684-691.
[5] Endres F, Hess J, Sturm J, et al.3-D Mapping With an RGB-D Camera[J]. IEEE Transactions on Robotics (S1552-3098), 2014, 30(1): 177-187.
[6] Lowe D G.Distinctive Image Features from Scale- Invariant Keypoints[C]//International Journal of Computer Vision. 2004: 91-110.
[7] Bay H, Tuytelaars T, Gool L V.SURF: Speeded Up Robust Features[J]. Computer Vision & Image Understanding (S1077-3142), 2006, 110(3): 404-417.
[8] Rublee E, Rabaud V, Konolige K, et al.ORB: An efficient alternative to SIFT or SURF[C]//International Conference on Computer Vision. IEEE Computer Society, 2011: 2564-2571.
[9] Besl P J, Mckay N D.Method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 1992, 14(2): 239-256.
[10] Angeli A, Filliat D, Doncieux S, et al.A Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words[J]. IEEE Transactions on Robotics (S1552-3098), 2008, 24(5): 1027-1037.
[11] Labbe M, Michaud F.Online global loop closure detection for large-scale multi-session graph-based SLAM[C]//Ieee/rsj International Conference on Intelligent Robots and Systems. 2014: 2661-2666.
[12] Kümmerle R, Grisetti G, Strasdat H, et al.G 2 o: A general framework for graph optimization[C]//IEEE International Conference on Robotics & Automation. 2011: 3607-3613.

基于RGB-D图像的三维同步定位与建图研究

3D Simultaneous Localization and Mapping Based on RGB-D Images

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