Real-Time visual loop-closure detection using fused iterative close point algorithm and extended Kalman filter

In this paper a new method will be proposed of determining the dynamic position of a robot in a relative coordinate system based on Kalman filtering, on a history of camera positions and on the robot's movements, on symbolic (semantic) tags. In order to track the robot's reiterated passage of one and the same place, it is necessary to carry out, at each step, the matching of the robot's position and state with the previous steps (the problem of "loop closure" - a loop closure and global optimization step). In the event of data coincidence, it is necessary to carry out adjusting the movement and refining a three-dimensional map of the environment. One of the known solutions of this problem will be taken as a basis and improved in the present work based on the algorithm of "the basket of words". We evaluate the RGB-D Loop-closure detection in indoor environments of Chelyabinsk State University.