Critical point recognition for vision-based coverage of unknown environments

This paper presents a critical point recognition method based on fuzzy logic and hidden Markov model (HMM) that can be applied in robot coverage of unknown environments. The method uses monocular camera to acquire omni-directional images of the environment where the robot locates. Adopted center-surround difference method, the salient local image regions are extracted from the images as landmarks. These landmarks are organized by using HMM to represent the critical point scene where the robot is, and fuzzy logic strategy is used to match the critical point scene and landmarks. This way, the problem of critical point sensing, which is the key problem in cell decomposition of robot coverage, can be converted into the evaluation problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of critical point sensing in unknown environments.