Mobile Robot Map Building Based on Cellular Automata

With the background of robotic mapping in unknown environment, this paper proposes measures to improve matching rate with SIFT, creates a novel cellular automata model of unknown environment and achieve cellular automata navigation. It consists of three parts. On the first, the paper introduces cellular automata environment model. On the second, scale-invariant image feature is used to extract environment feature in room, and also It proposed an approach to improve error matched pixel. At last, due to sensor model for binocular stereo vision sensor, local 3D coordinates of features was converted to global 2D coordinates to get a feature map, through validating and clustering features in feature map, we get the cellular automata map, at the same time the paper finished the cellular automata navigation. Aiming at complex environment in room. The performance of the proposed algorithm was verified by experiment in home environment with obstacle.