[1] du Toit N E, Burdick J W. Robot motion planning in dynamic, uncertain environments[J]. IEEE Transactions on Robotics, 28, 101-115(2012).

[2] Chen X J, Ye T, Li L et al. Nonvisual sensor and its signal processing method for a mobile robot[J]. Robot, 25, 313-317(2003).

[3] Zhang K J, Zhang Y Z, Lü G H et al. Loop closure detection based on local semantic topology for visual SLAM system[J]. Robot, 41, 649-659(2019).

[4] Xu X S, Liu X H, Zhao B C et al. An extensible positioning system for locating mobile robots in unfamiliar environments[J]. Sensors, 19, 4025(2019). http://www.ncbi.nlm.nih.gov/pubmed/31540461

[5] Loo S Y, Amiri A J, Mashohor S et al. CNN-SVO: improving the mapping in semi-direct visual odometry using single-image depth prediction[C]. //2019 International Conference on Robotics and Automation (ICRA), May 20-24, 2019, Montreal, QC, Canada, 5218-5223(2019).

[6] Wang Y B, Huang S D. Motion segmentation based robust RGB-D SLAM[C]. //Proceeding of the 11th World Congress on Intelligent Control and Automation, June 29-July 4, 2014, Shenyang, China., 3122-3127(2014).

[7] Sun Y X, Liu M, Meng M Q H. Improving RGB-D SLAM in dynamic environments:a motion removal approach[J]. Robotics and Autonomous Systems, 89, 110-122(2017). http://www.sciencedirect.com/science/article/pii/S0921889015302232

[8] Wang J, Wu X X, Guo B L. Robot path planning using improved particle swarm optimization[J]. Computer Engineering and Applications, 48, 240-244(2012).

[9] Li S D, Ding M Y, Cai C et al. Efficient path planning method based on genetic algorithm combining path network[C]. //2010 Fourth International Conference on Genetic and Evolutionary Computing, December 13-15, 2010, Shenzhen, China., 194-197(2010).

[10] Tao T, Huang Y L, Sun F C et al. Motion planning for SLAM based on frontier exploration[C]. //2007 International Conference on Mechatronics and Automation, August 5-8, 2007, Harbin, China., 2120-2125(2007).

[11] Fox D, Burgard W, Kruppa H et al. A probabilistic approach to collaborative multi-robot localization[J]. Autonamous Robots, 8, 325-344(2000). http://link.springer.com/article/10.1023/A:1008937911390

[12] Mozos O M, Stachniss C, Burgard W. Supervised learning of places from range data using AdaBoost[C]. //Proceedings of the 2005 IEEE International Conference on Robotics and Automation, April 18-22, 2005, Barcelona, Spain., 1730-1735(2005).

[13] He S B, Sun K H. Design and optimization of micro-mouse solving the maze algorithm based on central method[J]. Computer Systems & Applications, 21, 79-82(2012).

[14] Freund Y, Schapire R E. A desicion-theoretic generalization of on-line learning and an application to boosting[M]. //Vitányi P. Computational learning theory. Lecture notes in computer science, 904, 23-37(1995).