Fast registration of laser scans with 4-point congruent sets - what works and what doesn't
Keywords: Point cloud registration, laser scanning, 3D feature extraction
Abstract. Sampling-based algorithms in the mould of RANSAC have emerged as one of the most successful methods for the fully automated registration of point clouds acquired by terrestrial laser scanning (TLS). Sampling methods in conjunction with 3D keypoint extraction, have shown promising results, e.g. the recent K-4PCS (Theiler et al., 2013). However, they still exhibit certain improbable failures, and are computationally expensive and slow if the overlap between scans is low. Here, we examine several variations of the basic K-4PCS framework that have the potential to improve its runtime and robustness. Since the method is inherently parallelizable, straight-forward multi-threading already brings down runtimes to a practically acceptable level (seconds to minutes). At a conceptual level, replacing the RANSAC error function with the more principled MSAC function (Torr and Zisserman, 2000) and introducing a minimum-distance prior to counter the near-field bias reduce failure rates by a factor of up to 4. On the other hand, replacing the repeated evaluation of the RANSAC error function with a voting scheme over the transformation parameters proved not to be generally applicable for the scan registration problem. All these possible extensions are tested experimentally on multiple challenging outdoor and indoor scenarios.