A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes

Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constraints, and it can be incorporated into the SLAM process as a data filtering process. In order to verify the proposed method, we conducted sufficient experiments on the public TUM RGB-D dataset and a sequence image dataset from our Kinect V1 camera; both were acquired in common dynamic indoor scenes. The detailed experimental results of our improved RGB-D SLAM were summarized and demonstrate its effectiveness in dynamic indoor scenes.