将曼哈顿世界假说（Manhattan World assumption，MW）引入室内定位问题，提出了一种改进的基于RGB-D视觉与平面特征的室内定位方案，不仅能有效提高场景匹配的成功率，还可简化未知场景下的定位问题，提高定位效率和实时性，可用于对同步定位与建图SLAM（SimultaneousLocalization and Mapping）系统的扩展.创新点主要体现在：针对解释树匹配的时间开销随特征数指数级上升的问题，设计了根据曼哈顿帧的主方向进行分解的匹配方法；针对单条行进路径搜索效率有待提高的问题，提出了在初始位姿确定后采用4自由度的简化定位方案；针对单帧中遍历执行子图匹配耗时较长的问题，将小范围子图合并为大范围子图后进行匹配.实验结果表明，该方案相较已有的平面特征定位方法，能缩短成功定位所需的行进距离，并显著降低单条行进路径上的平均搜索耗时.
Using the Manhattan World assumption with plane-based indoor localization, an indoor positioning scheme based on plane features in RGB-D vision is proposed, which can be used to extend SLAM (Simultaneous Localization and Mapping) systems. A matching process based on the main direction of the Manhattan Frame is designed to reduce the exponentially increasing time consumed. Simplified localization with 4 degrees of freedom is adopted after the initial pose determination for the problem of low efficiency during exploration. Small subgraphs in each frame are merged into one subgraph for matching to reduce the time consumed for repetitive subgraph matching. The proposed scheme not only effectively increases the success rate of scene matching, but also simplifies positioning in unknown scenes and improves positioning efficiency. Experimental results show that the method can achieve successful localization with shorter path lengths and reduce computational cost for real-time applications.
[1] GLOCKER B, SHOTTON J, CRIMINISI A, et al. Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding[J]. IEEE transactions on visualization and computer graphics, 2015, 21(5):571-583.
[2] SE S, LOWE D G, LITTLE J J. Vision-based global localization and mapping for mobile robots[J]. IEEE Transactions on robotics, 2005, 21(3):364-375.
[3] 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, 2017, 33(5):1255-1262.
[4] SALAS-MORENO R F, NEWCOMBE R A, STRASDAT H, et al. Slam++:Simultaneous localisation and mapping at the level of objects[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2013:1352-1359.
[5] SHI Y, XU K, NIEßNER M, et al. Planematch:Patch coplanarity prediction for robust rgb-d reconstruction[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018:750-766.
[6] FORSTNER W, KHOSHELHAM K. Efficient and accurate registration of point clouds with plane to plane correspondences[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017:2165-2173.
[7] TAGUCHI Y, JIAN Y D, RAMALINGAM S, et al. Point-plane SLAM for hand-held 3D sensors[C]//2013 IEEE International Conference on Robotics and Automation. IEEE, 2013:5182-5189.
[8] FERNÁNDEZ-MORAL E, RIVES P, ARÉVALO V, et al. Scene structure registration for localization and mapping[J]. Robotics and Autonomous Systems, 2016, 75:649-660.
[9] SALAS-MORENO R F, GLOCKEN B, KELLY P H J, et al. Dense planar SLAM[C]//2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2014:157-164.
[10] HSIAO M, WESTMAN E, ZHANG G, ET AL. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017:5110-5117.
[11] MA L, KERL C, STÜCKLER J, et al. CPA-SLAM:Consistent plane-model alignment for direct RGB-D SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016:1285-1291.
[12] CHO H G, YEON S, CHOI H, et al. Detection and compensation of degeneracy cases for IMU-kinect integrated continuous SLAM with plane features[J]. Sensors, 2018, 18(4):935(9pages).
[13] COUGHLAN J M, YUILLE A L. Manhattan world:Compass direction from a single image by bayesian inference[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999, 2:941-947.
[14] SCHINDLER G, DELLAERT F. Atlanta world:An expectation maximization framework for simultaneous lowlevel edge grouping and camera calibration in complex man-made environments[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04). IEEE, 2004, 1:I-I.
[15] STRAUB J, ROSMAN G, FREIFELD O, et al. A mixture of manhattan frames:Beyond the manhattan world[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014:3770-3777.
[16] RUSU R B, Cousins S. 3D is here:Point Cloud Library (PCL)[C]//IEEE International Conference on Robotics & Automation. 2011:1-4.
[17] GRIMSON W E L, LOZANO-PEREZ T. Localizing overlapping parts by searching the interpretation tree[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(4):469-482.
中国综合性科技类核心期刊(北大核心)