The performances of traditional visual odometry algorithms may degenerate when a scene contains dynamic objects. In this paper, we propose a novel spatiotemporal visual odometry for dynamic indoor environments by using RGB-D cameras. First, to improve the data association, the complete ground plane is detected and used in the optimization function of coarse pose estimation. Then, the spatial information and temporal information are fused by an undirected network to improve the segmentation of the moving objects. Last, the pose is computed accurately via a coarse-to-fine strategy. Experimental results that demonstrate the performance of the proposed method are presented, and the factors that affect the measurement accuracy are analyzed.

当场景包含动态对象时，传统的视觉测距算法的性能可能会降低。在本文中，我们通过使用RGB-D摄像机提出了一种针对动态室内环境的新型时空视觉测距法。首先，为了改善数据关联性，检测完整的地面并将其用于粗略姿态估计的优化功能。然后，空间信息和时间信息由无向网络融合，以改善运动对象的分割。最后，通过从粗到精的策略准确地计算出姿势。实验结果表明了该方法的有效性，并分析了影响测量精度的因素。