In this paper, we conducted a comparative evaluation of three RGB-D SLAM (Simultaneous Localization and Mapping) algorithms: RTAB-Map, ORB-SLAM3, and OpenVSLAM for SURENA-V humanoid robot localization and mapping. Our test involves the robot to follow a full circular pattern, with an Intel RealSense D435 RGB-D camera installed on its head. In assessing localization accuracy, ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map at 0.1641 and OpenVSLAM at 0.1847. However, it should be noted that both ORB-SLAM3 and OpenVSLAM faced challenges in maintaining accurate odometry when the robot encountered a wall with limited feature points. Nevertheless, OpenVSLAM demonstrated the ability to detect loop closures and successfully relocalize itself within the map when the robot approached its initial location. The investigation also extended to mapping capabilities, where RTAB-Map excelled by offering diverse mapping outputs, including dense, OctoMap, and occupancy grid maps. In contrast, both ORB-SLAM3 and OpenVSLAM provided only sparse maps.

翻译：本文对三种RGB-D SLAM（同时定位与建图）算法——RTAB-Map、ORB-SLAM3与OpenVSLAM——在SURENA-V人形机器人定位与建图任务中进行了对比评估。测试中机器人沿完整圆形轨迹运动，头部搭载Intel RealSense D435 RGB-D相机。在定位精度评估中，ORB-SLAM3以0.1073的ATE（绝对轨迹误差）表现最优，其次为RTAB-Map（0.1641）与OpenVSLAM（0.1847）。然而需注意，当机器人遭遇特征点有限的墙壁时，ORB-SLAM3与OpenVSLAM均难以维持精准里程计。尽管如此，OpenVSLAM展现出闭环检测能力，并在机器人靠近初始位置时成功实现地图内重定位。研究进一步拓展至建图能力评估，RTAB-Map表现卓越，可生成稠密地图、八叉树地图与占据栅格地图等多种建图输出；而ORB-SLAM3与OpenVSLAM仅能提供稀疏地图。