室内环境中移动机器人多种SLAM系统的比较
Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment
AI总结：
本文构建搭载激光雷达与单目、立体相机的移动机器人，在同一办公室数据集上评测多类基于ROS的SLAM系统，并指出Cartographer、单目ORB SLAM与RTAB Map表现突出。
摘要：
本文对由多种基于ROS的SLAM系统计算得到的移动机器人轨迹进行了比较分析。为此，我们开发了一个配备常见传感器的移动机器人原型：2D激光雷达、单目相机以及ZED立体相机。随后，我们在典型的办公环境中开展实验，采集所有传感器的数据，并基于所获取的数据集运行所有受测SLAM系统。我们研究了以下SLAM系统：（a）基于2D激光雷达的：GMapping、Hector SLAM、Cartographer；（b）基于单目相机的：Large Scale Direct monocular SLAM（LSD SLAM）、ORB SLAM、Direct Sparse Odometry（DSO）；（c）基于立体相机的：ZEDfu、Real-Time Appearance-Based Mapping（RTAB map）、ORB SLAM、Stereo Parallel Tracking and Mapping（S-PTAM）。由于所有SLAM方法均在同一数据集上进行测试，我们采用适当的指标对不同SLAM系统的结果进行了比较，结果表明基于激光雷达的Cartographer SLAM、单目ORB SLAM和立体RTAB Map方法取得了令人鼓舞的结果。
英文摘要：
This article presents a comparative analysis of a mobile robot trajectories computed by various ROS-based SLAM systems. For this reason we developed a prototype of a mobile robot with common sensors: 2D lidar, a monocular and ZED stereo cameras. Then we conducted experiments in a typical office environment and collected data from all sensors, running all tested SLAM systems based on the acquired dataset. We studied the following SLAM systems: (a) 2D lidar-based: GMapping, Hector SLAM, Cartographer; (b) monocular camera-based: Large Scale Direct monocular SLAM (LSD SLAM), ORB SLAM, Direct Sparse Odometry (DSO); and (c) stereo camera-based: ZEDfu, Real-Time Appearance-Based Mapping (RTAB map), ORB SLAM, Stereo Parallel Tracking and Mapping (S-PTAM). Since all SLAM methods were tested on the same dataset we compared results for different SLAM systems with appropriate metrics, demonstrating encouraging results for lidar-based Cartographer SLAM, Monocular ORB SLAM and Stereo RTAB Map methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
