Autonomous Navigation System for Indoor Mobile Robots Based on a Multi-sensor Fusion Technology

计算机科学 运动规划 计算机视觉 人工智能 惯性测量装置 激光雷达 网格参考 同时定位和映射 点云 机器人 移动机器人 实时计算 构造(python库) 占用网格映射 移动机器人导航 传感器融合 路径(计算) 机器人控制 地理 遥感 程序设计语言
作者
Hongcheng Wang,Niansheng Chen,Dingyu Yang,Guangyu Fan
出处
期刊:Communications in computer and information science 卷期号:: 502-517
标识
DOI:10.1007/978-981-19-4546-5_39
摘要

Map construction and path planning are two critical problems for an autonomous navigation system. One traditional map construction method is to construct a 2D grid map based on LiDAR, but this method has some limits. It easily ignores 3D information which affects the accuracy of navigation. Another one is visual SLAM techniques, such as ORB-SLAM2 and S-PTAM algorithms, which can recognize 3D objects. But the visual methods perform not well because of light changes. Some conventional path planning algorithms, such as TEB and DWA, are proposed for auto-navigation. However, those algorithms are likely to go to a stalemate due to local optimum, or have the problems of collision caused by sudden speed changes in constrained environments. In order to address these issues, this paper proposes a multi-sensor fusion method for map construction and autonomous navigation. Firstly, the fusion model combines RGB-D, lidar laser, and inertial measurement unit (IMU) to construct 2D grid maps and 3D color point cloud maps in real-time. Next, we present an improved local planning algorithm (Opt_TEB) to solve the velocity mutation problem, enabling the robot to get a collision-free path. We implemented the whole system based on the ROS framework, which is a wide used an open-source robot operating system. The map construction and path planning algorithms are running on the robot, while the visualization and control modules are deployed on a back-end server. The experimental results illustrate that the multi-sensor fusion algorithm is able to conform to the original map more than the 2D grid map. Furthermore, our improved algorithm Opt_TEB performs smoothly and has no collision with obstacles in 30 trials. The navigation speed is improved by 4.2% and 11.5% compared to TEB and DWA, respectively.
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