SegMatch: Segment based loop-closure for 3D point clouds
This addresses loop-closure detection for robotics and autonomous systems in large-scale, unstructured environments, representing a novel method for a known bottleneck.
The authors tackled loop-closure detection in 3D point clouds by proposing SegMatch, which matches 3D segments to balance local and global features, achieving accurate localization at 1Hz on the largest KITTI odometry dataset sequence.
Loop-closure detection on 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of "perfect segmentation", or on the existence of "objects" in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm will be made available after publication.