Design of autonomous localization and navigation system for indoor mobile robot

Abstract

Abstract:

An autonomous localization and navigation system is designed for omnidirectional indoor mobile robots, which takes NVIDIA Jetson TX2 as upper computing platform and senses surrounding environment by lidar. Integrating wheel odometer and single axis inertial measurement unit, the designed system locates the robot by using selfadaptive Monte -Carlo algorithm basing on particle wave filter and implements autonomous navigation and obstacles avoidance by A-star algorithm and TEB algorithm. Results of Stage simulation and field experiment both show that the designed system can autonomously and accurately locate the robot, construct surrounding map with high accuracy, plan optimal path and help the robot avoid dynamic obstacles in time.

Key words: mobile robot, simultaneous localization and mapping, indoor localization and navigation, lidar, robot operating
system

CLC Number:

TP242

ZHOU Xulong, ZHAO Yanzheng, YANG Yuemin. Design of autonomous localization and navigation system for indoor mobile robot[J]. Journal of Civil Aviation University of China, 2020, 38(5): 55-59.

References 17

[1]	CHARALAMPOUS K, KOSTAVELIS I, GASTERATOS A. Recent trends in social aware robot navigation: a survey[J]. Robotics and Autono-mous Systems, 2017, 93: 85-104.
[2]	MUR-ARTAL R, MONTIEL J M M, TARDOS J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions onRobotics, 2015, 31(5): 1147-1163.
[3]	ZHENG FANG, LIU YUNHUI. Visual-odometric localization and mapping for ground vehicles using SE(2)-XYZ constraints[C]//2019 International Conference on Robotics and Automation(ICRA）, Montreal,Canada, May 20-24, 2019: 3556-3562.
[4]	QIN TONG, LI PEILIANG, SHEN SHAOJIE. VINS-Mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.
[5]	FORSTER C, PIZZOLI M, SCARAMUZZA D. SVO: fast semi-direct monocular visual odometry[C]//2014 IEEE International Conference on Robotics and Automation(ICRA）, Hong Kong, China, May 31-Jun 7,2014: 15-22.
[6]	高翔. 视觉SLAM 十四讲[M]. 北京：电子工业出版社, 2017.
[7]	CASTELLANOUS J A, MARTINEZ-CANTIN R, TARDOS J D, et al.Robocentric map joining: improving the consistency of EKF-SLAM[J].Robotics & Autonomous Systems, 2007, 55(1): 21-29.
[8]	JIANG WEICHENG援Implementation of odometry with EKF in hector SLAM methods[J]援International Journal of Automation 驭Smart Technology,2018, 8(1): 9-18.
[9]	SEBASTIAN THRUN, WOLFRSM BURGARD, DIETER FOX. Probabilistic Robotics[M]. 北京: 机械工业出版社, 2017.
[10]	徐曙. 基于SLAM 的移动机器人导航系统研究[D]. 武汉: 华中科技大学, 2014.
[11]	寿佳鑫, 张志明, 苏永清, 等. 基于ROS 和激光雷达的室内移动机器人定位和导航系统设计与实现[J]. 机械与电子, 2018, 36(11): 78-82.
[12]	CHENG YI, WANG GONGYE. Mobile robot navigation based on lidar[C]//2018 Chinese Control and Decesion Conference(CCDC), Shenyang,China, Jun 9-11, 2018: 1243-1246.
[13]	FOX D, BURGARD W, THRUN S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.
[14]	R魻SMANN C, HOFFMANN F, BERTRAM T. Integrated online trajectory planning and optimization in distinctive topologies[J]. Robotics and Autonomous Systems, 2016, 88：142-153.
[15]	胡春旭, 熊枭, 任慰, 等. 基于嵌入式系统的室内移动机器人定位与导航[J]. 华中科技大学学报(自然科学版), 2013, 41(S1): 254-257.
[16]	胡春旭. ROS 机器人开发实践[M]. 北京：机械工业出版社, 2018.
[17]	SZCZERBA R J, GALKOWSKI P, GLICKTEIN I S, et al. Robust algorithm for real-time route planning[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 869-878.