基于深度相机的SLAM算法评测

Evaluation of the depth camera based SLAM algorithms

针对3种典型的基于深度相机的同步定位与地图构建(SLAM)算法,包括RGB-D SLAM V2,RTAB-Map和DVO SLAM,介绍这3种SLAM算法的理论特点。采用两个公开的SLAM数据集,包括TUM数据集和ICL-NUIM数据集,进行SLAM算法的评测,评测指标包括SLAM算法的精确度、运行性能以及鲁棒性。评测的实验结果表明,在选择基于深度相机的SLAM算法时:如果考虑精确度和鲁棒性优先于运行性能,则选择RGB-D SLAM V2;如果考虑运行性能和鲁棒性优先于精确度,则选择DVO SLAM;如果考虑精确度和运行性能优先于鲁棒性,则选择RTAB-Map。

Three typical depth camera based simultaneous localization and mapping(SLAM)algorithms,including RGBD SLAM V2,RTABMap and DVO SLAM,whose theories and features were introduced.By using two opensource SLAM datasets,including TUM dataset and ICLNUIM dataset,the above three SLAM algorithms were evaluated,and the index included the accuracy,performance and robustness of the SLAM algorithms.The results of the experiments demonstrate that RGBD SLAM V2is chosen when accuracy and robustness are prior to speed;DVO SLAM is chosen when speed and robustness are prior to accuracy;RTABMap is chosen when speed and accuracy are prior to robustness.