关键词: RGB-D SLAM算法, 四叉树, ORB, 关键帧, 回环检测, 光速法平差

Abstract:

Feature-based visual SLAM（Simultaneous Localization and Mapping） has some problems such as poor real-time and robustness. In this paper, an improved ORB feature extraction method based on quad-tree is proposed, and a robot RGB-D SLAM algorithm including frontend, backend and map construction is designed. In the frontend, quad-tree is used to extract uniform ORB features, and hamming distances between descriptors are calculated to achieve feature matching. According to the idea of random sampling consistency algorithm, EPNP（Efficient Perspective-N-Point） and iterative closest point method are combined to obtain accurate robotic poses through multiple iterations. To reduce the cumulative error, the keyframe loop closing is adopted to optimize the pose graph based on bundle adjustment so that a globally consistent 3D map is constructed. Experiments on TUM datasets and multi-caterpillar omnidirectional mobile robot are carried out to verify the validity of the presented approach. The results meet the real-time and stability requirements. The feasibility and effectiveness of the algorithm are proved.

Key words: RGB-D Simultaneous Localization and Mapping（SLAM） algorithm, quad-tree, ORB, keyframe, loop closing, bundle adjustment