Abstract: To solve the accurate positioning problem of mobile robots in dynamic indoor environments, a semi-direct RGBD visual SLAM (simultaneous localization and mapping) algorithm integrating a motion detection algorithm, is proposed. It consists of three parts, including motion detection, camera pose estimation and dense map building based on TSDF (truncated signed distance function) model. Firstly, the primary camera pose can be estimated with the sparse image alignment algorithm by minimizing the photometric residual. Then, the pose estimation of visual odometry is used to compensate for image movement. A Gaussian model is built based on real-time update of the image patch. The moving object in the image can be segmented according to the variance change of each patch. By eliminating the local map points projected on the motion region and minimizing the reprojection error, the camera pose can be optimized again to improve its estimation accuracy. Finally, the dense TSDF map can be built with the camera pose and RGB-D image. Furthermore, the map can be updated in real time in dynamic environments by using the motion detection result and and the color change of voxel patches in the map. Experimental results show that the proposed algorithm can effectively improve the precision of camera pose estimation and implement the real-time update of the dense map in dynamic indoor environments. Therefore, it can improve the system robustness and the scene reconstruction accuracy.