3D Visual SLAM Based on Multiple Iterative Closest Point

With the development of novel RGB-D visual sensors, data association has been a basic problem in 3D Visual Simultaneous Localization and Mapping (VSLAM). To solve the problem, a VSLAM algorithm based on Multiple Iterative Closest Point (MICP) is presented. By using both RGB and depth information obtained from RGB-D camera, 3D models of indoor environment can be reconstructed, which provide extensive knowledge for mobile robots to accomplish tasks such as VSLAM and Human-Robot Interaction. Due to the limited views of RGB-D camera, additional information about the camera pose is needed. In this paper, the motion of the RGB-D camera is estimated by a motion capture system after a calibration process. Based on the estimated pose, the MICP algorithm is used to improve the alignment. A Kinect mobile robot which is running Robot Operating System and the motion capture system has been used for experiments. Experiment results show that not only the proposed VSLAM algorithm achieved good accuracy and reliability, but also the 3D map can be generated in real time.