Abstract
Point cloud registration is a crucial step and gaining more importance in many challenging 3D computer vision tasks including 3D reconstruction, autonomous navigation, 3D object recognition and remote sensing. In this work, we proposed a highly discriminative local feature descriptor named Local Point Feature Histogram (LPFH) for 3D point cloud registration. LPFH formulates a simple and comprehensive histogram for surface representation, which encompassed a 3D descriptor. Based on the proposed LPFH, we use Random Sample Consensus (RANSAC) algorithm to form our coarse registration stage, followed by an Iterative Closest Point (ICP) fine registration stage, these two steps form our registration algorithm. Validations and comparisons with other point cloud registration algorithms showed that LPFH is low-dimension, efficient, effective and easy to compute.
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