A deep learning and depth image based obstacle detection and distance measurement method for substation patrol robot

Abstract

Recently, substation patrol robot is gradually used to replace the manual inspection in order to improve the inspection efficiency as well as security and automation level of substation maintenance. The research of obstacle avoidance is a hot spot in substation intelligent patrol robot area. The emerging new generation of artificial intelligence (AI) technology provides a new way to solve the obstacle detection and distance measurement problem. To realize accurate, effective and real-time response to the environmental changes, a novel obstacle avoidance method based on deep learning and depth image is proposed. The core of this method is pixel-level instance segmentation between obstacles and roads, along with a pixel-level matching of obstacles' segmentation mask and depth data. The effectiveness of the proposed method is validated by actual tests in real substation environment.