A robust RGB-D visual odometry with moving object detection in dynamic indoor scenes

Simultaneous localisation and mapping (SLAM) are the basis for many robotic applications. As the front end of SLAM, visual odometry is mainly used to estimate camera pose. In dynamic scenes, classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results. In order to improve the robustness of visual odometry in dynamic scenes, this paper proposed a dynamic region detection method based on RGB-D images. Firstly, all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively. Meanwhile, the depth image is clustered using the K-Means method. The classified feature points are mapped to the clustered depth image, and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points. Subsequently, a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image, and the feature points covered by the mask are all removed. The remaining static feature points are applied to estimate the camera pose. Finally, some experimental results are provided to demonstrate the feasibility and performance.