Robust Point Cloud Registration with Geometry-based Transformation Invariant Descriptor

Abstract

This work presents a novel method for point registration in 3D space. The proposed algorithm utilizes transformation-invariant geometry information to estimate the pose of objects based on correspondences between points in two sets. Conventional methods use geometry descriptors to find these correspondences, which can result in a large number of outliers. Most existing algorithms are error-prone when outliers are present. Instead of formulating point registration as a non-convex optimization problem, we propose an intuitive method that filters out spurious correspondences. This is achieved by evaluating three different geometry-based transformation-invariant descriptors for outlier removal. We construct fully connected graphs with the proposed descriptors on correspondences, and convert the outlier removal problem into a subgraph isomorphism problem that is solved using a binary clustering approach. The resulting inlier clustering is used to estimate the transformation between the two point sets. The effectiveness of the proposed approach is evaluated on standard 3D data and the 3DMatch scan matching dataset, and compared against existing state-of-the-art methods. Results show that our method effectively reduces outliers and performs similarly to these methods.