基于NDT匹配和改进回环检测的SLAM研究
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摘要
在利用即时定位与建图(SLAM)来对周围的环境进行三维建模的应用场景中,提出了一种改进的检测回环的方法。利用深度相机获得的三维点云数据进行正态分布变换(NDT)匹配,得到各个时刻间的初始相对位姿,之后利用随机抽样一致(RANSAC)算法来进一步提高初始匹配的精度。混合回环检测方法是利用空间位置的方法来锁定初始的候选匹配对象,减少全局搜索的耗时;利用基于外观相似性的方法对候选对象进行最后的判别,选择具有强鲁棒性的回环。对公开数据集及室内采集的数据进行实验,并与RGBD-SLAM和RTAB-MAP两种算法进行对比。结果表明,提出的方法原理正确,并在运行一段时间后,可获得比其他两种算法更快的闭环检测速度。
A improved loop-closure detection method for mobile robot Simultaneous Localization and Mapping(SLAM)is presented to address the problem of 3 D modeling in complex indoor environment.According to the camera calibration model and the image feature extraction and matching procedure,the association between two 3 Dpoint clouds can be established.On the basis of the Random Sample Consensus(RANSAC)algorithm,the correspondence based Normal Distribution Transform(NDT)algorithm arithmetic model is solved to realize the robot's initial localization effectively.The loop detection method,which combines the space based method and appearance similarity method,promotes the speed of detection.The key frame-to-frame selection mechanism is used for maintaining and updating the global map.Contrast experiments based on the datasets and the actual scene show that,compared with the RGBD-SLAM and RTABMAP,the improved hybrid loop-closure detection algorithm has better real-time performance.
A improved loop-closure detection method for mobile robot Simultaneous Localization and Mapping(SLAM)is presented to address the problem of 3 D modeling in complex indoor environment.According to the camera calibration model and the image feature extraction and matching procedure,the association between two 3 Dpoint clouds can be established.On the basis of the Random Sample Consensus(RANSAC)algorithm,the correspondence based Normal Distribution Transform(NDT)algorithm arithmetic model is solved to realize the robot's initial localization effectively.The loop detection method,which combines the space based method and appearance similarity method,promotes the speed of detection.The key frame-to-frame selection mechanism is used for maintaining and updating the global map.Contrast experiments based on the datasets and the actual scene show that,compared with the RGBD-SLAM and RTABMAP,the improved hybrid loop-closure detection algorithm has better real-time performance.
引文
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[2]贺伟,梁昔明.未知环境中移动机器人SLAM问题的研究进展[J].微计算机信息,2005,(3):179-180.He Wei,Liang Ximing.Research progress on SLAM problems of mobile robots in unknown environments[J].Microcomputer Information,2005,(3):179-180.
[3]Lee S,Lee S,Baek S.Vision-based kidnap recovery with SLAM for home cleaning robots[J].Journal of Intelligent&Robotic Systems,2012,67(1):7-24.
[4]Jaud M,Rouveure R,Faure P,et al.Method for orthorectification of terrestrial radar maps[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,97:185-194.
[5]邓志红,刘明阳,付梦印.一种改进的视觉传感器与激光测距雷达特征匹配点提取算法[J].光学技术,2010,36(1):43-47.Deng Zhihong,Liu Mingyang,Fu Mengyin.An improved matching algorithm for feature extraction of vision sensor and laser ranging radar[J].Optics Technology,2010,36(1):43-47.
[6]Ma J,Bajracharya M,Susca S,et al.Real-time pose estimation of a dynamic quadruped in GPS-denied environments for 24-hour operation[J].The International Journal of Robotics Research,2016,35(6):631-653.
[7]Fuentes-Pacheco J,Ruiz-Ascencio J,Rendón-Mancha J M.Visual simultaneous localization and mapping:a survey[J].Artificial Intelligence Review,2015,43(1):55-81.
[8]Xiao J,Gou J L,Ma X M,et al.A large viewing angle 3-dimensional V-SLAM algorithm with a Kinect-based mobile robot system[J].Robot,2014,36(5):560-568.
[9]Henry P,Krainin M,Herbst E,et al.RGB-D mapping:Using depth cameras for dense 3Dmodeling of indoor environments[C]∥12th International Symposium on Experimental Robotics,Berlin,Germany,2014:477-491.
[10]Mihalyi R G,Pathak K,Vaskevicius N,et al.Robust 3Dobject modeling with a low-cost RGBD-sensor and AR-markers for applications with untrained end-users[J].Robotics and Autonomous Systems,2015,66:1-17.
[11]Folkesson J,Christensen H I.Closing the loop with graphical SLAM[J].IEEE Transactions on Robotics,2007,23(4):731-741.
[12]Callmer J,Granstr M K,Nieto J,et al.Tree of words for visual loop closure detection in urban SLAM[C]∥2008Australasian Conference on Robotics and Automation,Canberra,Australia,2008:102.
[13]Cummins M,Newman P.Probabilistic Appearance based navigation and loop closing[C]∥IEEE International Conference on Robotics and Automation,IEEE,2007:2042-2048.
[14]Cummins M J,Newman P M.FAB-MAP:Probabilistic localization and mapping in the space of appearance[J].International Journal of Robotics Research,2008,27(6):647-665.
[15]Fischler M A,Bolles R C.Random sample consensus:aparadigm for model fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395.
[16]Mills S,Green R.Bag-of-words-driven,single-camera simultaneous localization and mapping[M].John Wiley and Sons Ltd.,2011.
[17]Konolige K,Bowman J,Chen J D,et al.View-based maps[J].The International Journal of Robotics Research,2010,29(8):941-957.
[18]Bosse M,Newman P,Leonard J,et al.An Atlas framework for scalable mapping[C]∥IEEE International Conference on Robotics and Automation,Piscataway,USA:IEEE,2003:1899-1906.
[19]Olson E B.Robust and effcient robotic mapping[D].Cambridge,USA:Department of Electrical Engineering and Computer Science,MIT,2008.
[20]Lu F,Milios E.Globally consistent range scan alignment for environment mapping[J].Autonomous Robots,1997,4(4):333-349.
[21]Kümmerle R,Grisetti G,Strasdat H,et al.G2O:A general framework for graph optimization[C]∥IEEE International Conference on Robotics and Automation,IEEE,2011:3607-3613.
[22]Angeli A,Doncieux S,Meyer J A,et al.Incremental visionbased topological SLAM[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems,IEEE,2008:1031-1036.
[23]Biber P,Strasser W.The normal distributions transform:a new approach to laser scan matching[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems,IEEE,2003,3:2743-2748.
[24]Galvez-López D,Tardos J D.Bags of binary words for fast place recognition in image sequences[J].IEEE Transactions on Robotics,2012,28(5):1188-1197.
[25]Sturm J,Engelhard N,Endres F,et al.A benchmark for the evaluation of RGB-D SLAM systems[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems,IEEE,2012:573-580.