针对光束法对初始值依赖性大以及双目相机模型的特点，在ORB-SLAM2算法基础提出了一种三阶段局部双目光束法平差算法。在帧间位姿跟踪阶段，为降低累积误差对匀速模型下3D-2D匹配的影响，引入环形匹配机制进一步剔除误匹配，并将关键帧地图点与当前帧3D-2D投影匹配；在跟踪局部地图阶段插入关键帧时，增加双目相机基距参数优化环节；在局部地图优化阶段，将最近的两关键帧间的普通帧也作为局部帧进行优化。KITTI数据集实验表明，三阶段局部双目光束法平差与ORB-SLAM2相比，构造了更多精确的3D-2D匹配，增加了优化约束条件，提高了运动估计与优化精度。
三阶段局部双目光束法平差视觉里程计
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出版日期：2018年11月1日
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参考文献
[1] Sibley G. Relative bundle adjustment[R]. Department of Engi-neering Science, Oxford University Technical Report. Oxford: Department of Engineering Science, Oxford University, 2009.
[2] Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007: 225–234.
[3] Strasdat H, Davison A J, Montiel J M M, et al. Double window optimisation for constant time visual SLAM[C]//2011 International Conference on Computer Vision, 2011: 2352–2359.
[4] Kümmerle R, Grisetti G, Strasdat H, et al. G2O: A general framework for graph optimization[C]//2011 IEEE International Conference on Robotics and Automation, 2011: 3607–3613.
[5] Galvez-López D, Tardos J D. Bags of binary words for fast place recognition in image sequences[J]. IEEE Transactions on Ro-botics, 2012, 28(5): 1188–1197.
[6] Bellavia F, Fanfani M, Pazzaglia F, et al. Robust selective stereo SLAM without loop closure and bundle adjust-ment[C]//Proceedings of the 17th International Conference on Image Analysis and Processing, 2013, 8156: 462–471.
[7] Badino H, Yamamoto A, Kanade T. Visual odometry by mul-ti-frame feature integration[C]//Proceedings of 2013 IEEE International Conference on Computer Vision Workshops, 2013: 222–229.
[8] Cvišić I, Petrović I. Stereo odometry based on careful feature selection and tracking[C]//European Conference on Mobile Robots. IEEE, 2015:1–6.
[9] Xu Y X, Chen F. Real-time stereo visual localization based on multi-frame sequence motion estimation[J]. Opto-Electronic Engineering, 2016, 43(2): 89–94.
许允喜, 陈方. 基于多帧序列运动估计的实时立体视觉定位[J]. 光电工程, 2016, 43(2): 89–94.
[10] Luo Y Y, Liu H L. Research on binocular vision odometer based on bundle adjustment method[J]. Control and Decision, 2016, 31(11): 1936–1944.
罗杨宇, 刘宏林. 基于光束平差法的双目视觉里程计研究[J]. 控制与决策, 2016, 31(11): 1936–1944.
[11] Lu W, Xiang Z Y, Liu J L. High-performance visual odometry with two-stage local binocular BA and GPU[C]//2013 IEEE Intelligent Vehicles Symposium (IV), 2013: 23–26.
[12] Mur-Artal R, Tardós J D. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 2016, 33(5): 1255–1262.
[13] Hou R B, Wei W, Huang T, et al. Indoor robot localization and 3D dense mapping based on ORB-SLAM[J]. Computer Applications, 2017, 37(5): 1439–1444.
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.
[14] Zhou S L, Wu X Z, Liu G, et al. Integrated navigation method of monocular ORB-SLAM/INS[J]. Journal of Chinese Inertial Technology, 2016, 24(5): 633–637.
周绍磊, 吴修振, 刘刚, 等. 一种单目视觉ORB-SLAM/INS组合导航方法[J]. 中国惯性技术学报, 2016, 24(5): 633–637.
[15] Lourakis M I A, Argyros A A. SBA: a software package for generic sparse bundle adjustment[J]. ACM Transactions on Mathematical Software (TOMS), 2009, 36(1): 2.
[16] Li Z, Liu J Y, Li H, et al. Feature detection and description algorithm based on ORB-LATCH[J]. Journal of Computer Applications, 2017, 37(6): 1759–1762, 1781.
李卓, 刘洁瑜, 李辉, 等. 基于ORB-LATCH的特征检测与描述算法[J]. 计算机应用, 2017, 37(6): 1759–1762, 1781.
[17] Fan J J, Liang H W, Zhu H, et al. Closed quadrilateral feature tracking algorithm based on binocular vision[J]. Robot, 2015, 37(6): 674–682.
樊俊杰, 梁华为, 祝辉, 等. 基于双目视觉的四边形闭环跟踪算法[J]. 机器人, 2015, 37(6): 674–682.
[18] Lu W. Research on key techniques of high-precision and real-time visual localization[D]. Hangzhou: Zhejiang University, 2015.
卢维. 高精度实时视觉定位的关键技术研究[D]. 杭州: 浙江大学, 2015.
[19] Geiger A, Lenz P, Stiller C, et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Re-search, 2013, 32(11): 1231–1237.
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基金项目：
国家自然科学基金资助项目(61203007，61304001)
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赵彤, 刘洁瑜, 李卓. 三阶段局部双目光束法平差视觉里程计[J]. 光电工程, 2018, 45(11): 180244.
