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Sampling-based algorithms in the mould of RANSAC have emerged as one of the most successful methods for the fully automatedregistration 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, andare computationally expensive and slow if the overlap between scans is low. Here, we examine several variations of the basic K-4PCSframework that have the potential to improve its runtime and robustness. Since the method is inherently parallelizable, straight-forwardmulti-threading already brings down runtimes to a practically acceptable level (seconds to minutes). At a conceptual level, replacingthe RANSAC error function with the more principled MSAC function (Torr and Zisserman, 2000) and introducing a minimum-distanceprior 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 theRANSAC error function with a voting scheme over the transformation parameters proved not to be generally applicable for the scanregistration problem. All these possible extensions are tested experimentally on multiple challenging outdoor and indoor scenarios

Fast registration of laser scans with 4-point congruent sets - what works and what doesn't