Abstract: 3D object recognition based on local feature description of point cloud is a challenging problem in the robotic. Although a large number of three-dimensional feature descriptors have been proposed, most of them use floating-point numbers, which are expensive for calculation and storage. In view of this, a high-performance three-dimensional local descriptor is proposed called binarized histogram of point-pair feature(B-HPPF). After downsampling the model, based on the point position and point normal information, extract seven features of each point pair in the local neighborhood; use these features to divide the local point pair set into several regions and information is ex-tracted. The features are binary-coded by comparing the size of the corresponding information. The binary sub-features of each region are combined in series to generate a final binary B-HPPF description. The B-HPPF descriptors are tested on a number of public datasets and compared with classical description algorithms. The results show that it is optimally balanced in terms of discriminative power, robustness, compactness and computational effi-ciency. Moerover, B-HPPF is applied to the target identification data set to verify its practicability.

Key words: point cloud, local feature, feature extrction, binarized, object recogntion