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A learning algorithm based on Hidden Markov random field model is designed, and three-dimensional data is extracted, and the robot is used as an experimental platform. In the process of recognition, obstacle areas and feasible areas are distinguished by template matching, and the accessible areas and obstacle areas of the scene around the robot are obtained by classification results. According to the state of the robot itself, the position and direction vectors of the robot are calculated. The robot adjusts its attitude under the control of the output vector, so as to avoid obstacles and move to the target point. Finally, it is proved that the experiment has ideal real-time performance, so it has an extensive practical application foundation.

A Realtime Algorithm for Distinguish Obstacle from Accessible Area in 3D Scene