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A Robust and Accurate Simultaneous Localization and Mapping System for RGB-D Cameras

机译：用于RGB-D相机的鲁棒和准确的同时定位和映射系统

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摘要

This paper presents a feature-based simultaneous localization and mapping (SLAM) system for RGB-D cameras that operates in real time, in indoor environments. The system is composed of three central components: tracking, mapping and loop closing which are based on ORB-SLAM2 [1]. The tracking part estimates the pose of a frame via optimizing both the reprojection error and inverse depth error of matching feature points, rather than just optimizing the reprojection error like ORB-SLAM2 [1]. The mapping part optimizes the pose of all keyframes and the position of all map points by using bundle adjustment, and an occupancy grid map of the surrounding environment is constructed based on the octree-based mapping framework OctoMap [2] for high-level tasks, and the map is more useful then the sparse points representation in ORB-SLAM2 [1]. The loop closing of ORB-SLAM2 [1] just uses color appearance information, but our system uses both the color appearance information and depth consistency to deal with inaccurate data association. The experiment results in the TUM RGB-D dataset [3] shows that our system achieves better localization accuracy and robustness than ORB-SLAM2 [1].
机译：本文为RGB-D相机提供了一种基于特征的同时定位和映射（SLAM）系统，该系统在室内环境中实时运行。该系统由三个中央组件组成：基于ORB-SLAM2 [1]的跟踪，映射和循环关闭。跟踪部分通过优化匹配特征点的重新注定误差和逆深度误差来估计帧的姿势，而不是仅优化ORB-SLAM2 [1]等重注错误。映射部分通过使用捆绑调整优化所有关键帧的姿势和所有地图点的位置，并且基于基于Octree的映射框架Octomap [2]为高级任务构建周围环境的占用网格图，并且该地图更有用，然后在ORB-SLAM2 [1]中的稀疏点表示。 ORB-SLAM2 [1]的循环关闭只是使用颜色外观信息，但我们的系统使用颜色外观信息和深度一致性来处理不准确的数据关联。实验结果在Tum RGB-D DataSet [3]表明我们的系统比ORB-SLAM2的定位精度和鲁棒性更好地实现了更好的本地化精度和鲁棒性。

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