Research on unstructured terrain semantic recognition of outdoor mobile robot

Mengna Lu; Guangzhu Chen; Yinhe Han; Rongsong Gou

doi:10.1117/12.2604533

5 October 2021 Research on unstructured terrain semantic recognition of outdoor mobile robot

Mengna Lu, Guangzhu Chen, Yinhe Han, Rongsong Gou

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110Y (2021) https://doi.org/10.1117/12.2604533
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

Whether the mobile robot can accurately recognize the terrain features will affect the motion control of the mobile robot. Since the outdoor unstructured terrains have not obvious boundary information, it is necessary to segment the terrain boundary. The paper builds the outdoor unstructured terrain dataset, then chooses the real-time semantic segmentation network-EDAN et to train and test the effort of the unstructured terrain segmentation. The experimental results show that the EDA et can achieve a better balance between segmentation accuracy and segmentation speed for unstrnctured terrain recognition.

Citation Download Citation

Mengna Lu, Guangzhu Chen, Yinhe Han, and Rongsong Gou "Research on unstructured terrain semantic recognition of outdoor mobile robot", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110Y (5 October 2021); https://doi.org/10.1117/12.2604533

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