Abstract: A fast point feature histogram descriptor algorithm combined with point cloud texture information is proposed to improve the feature extraction efficiency and matching accuracy in the point cloud matching and recognition process. Firstly, a shape feature histogram was constructed based on the fast point feature histogram descriptor and a texture feature histogram was constructed by using CIELab color space and multiple point-to-texture attribute metrics. Then the two feature histograms were connected to obtain a fast point feature histogram descriptor combined with point cloud texture information. The feature histogram descriptor was verified by using public point cloud data sets and real spot cloud data. and the feature matching test and point density change test were carried out for this feature descriptor and multiple existing descriptors. The test results show that the comprehensive performance of the descriptor is the best when the CIE00 color difference is used as the point-to-texture attribute metrics. The algorithm has a good feature description performance and high feature extraction efficiency and matching efficiency and it has strong robustness when the point cloud density changes.

Key words: point cloud, texture information, feature descriptor, feature matching, feature extraction efficiency, matching accuracy, density change