Autonomous obstacle avoidance method based on deep learning and stereoscopic vision

Abstract

The invention discloses an autonomous obstacle avoidance method based on deep learning and a binocular stereoscopic vision technology. Environment video information is collected in real time through a binocular camera, an obstacle three-dimensional detection algorithm is constructed based on the target detection in deep learning in combination with the binocular stereoscopic vision technology, and a robot performs autonomous obstacle avoidance according to an obtained obstacle avoidance steering decision, the three-dimensional coordinates of an obstacle and the obstacle avoidance angle. According to the method, deep learning target detection serves as a core, depth information of traditional machine vision is fused, the defect that only two-dimensional coordinate information can be obtained through a single deep learning algorithm is overcome, and complete three-dimensional coordinates are obtained. According to the invention, end-to-end learning in an obstacle avoidance scene is realized, complicated three-dimensional reconstruction and path planning problems are avoided, and the system cost is saved.

Description

technical field [0001] The invention belongs to the field of robot obstacle avoidance, in particular to an autonomous obstacle avoidance method based on deep learning and stereo vision. Background technique [0002] With the continuous development of science and technology, mobile robots have been widely used in various fields, including life services, industrial production, military, entertainment and so on. Robotics design control, machinery, computer and other disciplines. The robot's navigation and obstacle avoidance capabilities are important indicators that reflect the intelligence of the robot. [0003] In the article "Real-time Obstacle Avoidance for Manipulator AndMobileRobots", Khatib builds an artificial potential field, so that obstacles and target points can generate abstract repulsion and attraction to the robot in the artificial potential field, and jointly control the robot to avoid obstacles. The path planned by the artificial potential field method has th...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an autonomous obstacle avoidance method based on deep learning and stereo vision, which takes deep learning algorithm as the core, integrates the depth information of traditional machine vision, and solves the problem that a single deep learning algorithm can only obtain two-dimensional coordinate information Insufficient to obtain complete 3D coordinates, realize end-to-end learning of obstacle avoidance scenarios, and avoid complicated 3D reconstruction and path planning problems

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