Affiliation:
1. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Abstract
An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.
Funder
National Natural Science Foundation of China
Subject
General Computer Science,Control and Systems Engineering
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