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

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.