Registration of multiple range scans as a location recognition problem: hypothesis generation, refinement and verification

Abstract

This paper addresses the following version of the multiple range scan registration problem. A scanner with an associated intensity camera is placed at a series of locations throughout a large environment; scans are acquired at each location. The problem is to decide automatically which scans overlap and to estimate the parameters of the transformations aligning these scans. Our technique is based on (1) detecting and matching keypoints - distinctive locations in range and intensity images, (2) generating and refining a transformation estimate from each keypoint match, and (3) deciding if a given refined estimate is correct. While these steps are familiar, we present novel approaches to each. A new range keypoint technique is presented that uses spin images to describe holes in smooth surfaces. Intensity keypoints are detected using multiscale filters, described using intensity gradient histograms, and backprojected to form 3D keypoints. A hypothesized transformation is generated by matching a single keypoint from one scan to a single keypoint from another, and is refined using a robust form of the ICP algorithm in combination with controlled region growing. Deciding whether a refined transformation is correct is based on three criteria: alignment accuracy, visibility, and a novel randomness measure. Together these three steps produce good results in test scans of the Rensselaer campus.