  Visual slam technology is one of the key technologies for robot to explore
unknown environment independently. Accurate estimation of camera pose based on
visual sensor is the basis of autonomous navigation and positioning. However,
most visual slam algorithms are based on static environment assumption and
cannot estimate accurate camera pose in dynamic environment. In order to solve
this problem, a visual SLAM algorithm for indoor dynamic environment is
proposed. Firstly, some moving objects are eliminated based on the depth
information of RGB-D camera, and the initial camera pose is obtained by
optimizing the luminosity and depth errors, then the moving objects are further
eliminated. and, the initial static background is used for pose estimation
again. After several iterations, the more accurate static background and more
accurate camera pose is obtained. Experimental results show that, compared with
previous research results, the proposed algorithm can achieve higher pose
estimation accuracy in both low dynamic indoor scenes and high dynamic indoor
scenes.
