Research on obstacle distance measurement method of UAV based on image processing technology

Mingxia Lin

doi:10.1117/12.3014383

9 January 2024 Research on obstacle distance measurement method of UAV based on image processing technology

Mingxia Lin

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296912 (2024) https://doi.org/10.1117/12.3014383
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In order to understand the obstacle distance measurement method of UAV, a research on obstacle distance measurement method of UAV based on image processing technology is proposed. This paper proposes an obstacle detection algorithm based on edge extraction. The outline of the obstacle can be obtained by extracting the edge features in the image, and the size, shape and position of the obstacle can be obtained by using a rectangle to frame the outline. Then the distance of the obstacle can be extracted from the contour of the obstacle by using the depth map generated by the binocular camera. The millimeter wave radar in front is used to fuse ranging with the central area of binocular camera image to improve the update frequency of obstacle distance. Finally, the effectiveness of the obstacle avoidance control strategy is verified by the obstacle avoidance flight test, and the UAV can choose the optimal obstacle avoidance direction and successfully bypass when encountering obstacles. The comparison of obstacle avoidance results shows that the obstacle avoidance method in this paper is advanced and has certain engineering application value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingxia Lin "Research on obstacle distance measurement method of UAV based on image processing technology", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296912 (9 January 2024); https://doi.org/10.1117/12.3014383

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