Research on semantic segmentation method of 3D point cloud for UAV landing

Yi Tian; Kai Wang; Gengchen Lv; Yaohong Qu; Chengxiang Wang; Kaijiang Wang

doi:10.1117/12.3040439

10 September 2024 Research on semantic segmentation method of 3D point cloud for UAV landing

Yi Tian, Kai Wang, Gengchen Lv, Yaohong Qu, Chengxiang Wang, Kaijiang Wang

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570T (2024) https://doi.org/10.1117/12.3040439
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Autonomous landing technology for UAVs is an important means to ensure its safety, while the on-board point cloud is often sparse and inhomogeneous. The paper proposes a neighbourhood geometric feature-enhanced point cloud semantic segmentation neural network for the problem of the difficulty of extracting local geometric features of the point cloud. The algorithm introduces the FPFH feature information into the neighbourhood feature encoding process of the point cloud semantic segmentation network PointNet⁺⁺, which enhances the network's description of the geometric relationship between points and points in the local neighbourhood. At the same time, the farthest point sampling method used in the original sampling process of the network is changed to random sampling to compensate for the encoding time of the geometric features. Then, the network of this paper and several other networks are trained and tested on the outdoor public dataset Semantic3D. The final results show that the overall accuracy and Mean Intersection over Union of the segmentation results of the network of this paper are 87.9% and 61.2%, respectively, which are in line with the requirements of the autonomous landing and descent applications of UAVs.

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

Citation Download Citation

Yi Tian, Kai Wang, Gengchen Lv, Yaohong Qu, Chengxiang Wang, and Kaijiang Wang "Research on semantic segmentation method of 3D point cloud for UAV landing", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570T (10 September 2024); https://doi.org/10.1117/12.3040439

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KEYWORDS

Point clouds

Semantics

Feature extraction

Image segmentation

Education and training

Unmanned aerial vehicles

Buildings

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