对比多种SLAM系统在室内机器人导航中的表现
Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment
- 使用统一数据集测试8种基于ROS的SLAM算法
- 激光雷达版Cartographer、单目ORB SLAM和双目RTAB Map表现最佳
- 适合机器人导航与SLAM算法选型研究者参考
本文对基于ROS的多种SLAM系统在移动机器人轨迹估计中的表现进行了对比分析。为此，我们搭建了一台配备2D激光雷达、单目相机和ZED双目相机的原型机器人，在典型办公环境中采集多传感器数据，并在相同数据集上运行所有测试的SLAM系统。比较了三类系统：(a) 2D激光雷达：GMapping、Hector SLAM、Cartographer；(b) 单目相机：LSD SLAM、ORB SLAM、DSO；(c) 双目相机：ZEDfu、RTAB-Map、ORB SLAM、S-PTAM。通过统一数据集与标准指标对比，结果表明激光雷达版Cartographer、单目ORB SLAM及双目RTAB-Map表现尤为出色。
原文摘要 · Abstract (English)
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.
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