SegMatch: Segment based loop-closure for 3D point clouds

TL;DR

SegMatch introduces a 3D segment-based loop-closure detection method that balances local and global features, enabling reliable, real-time localization in large, unstructured environments without requiring perfect segmentation.

Contribution

The paper presents SegMatch, a novel loop-closure detection algorithm using 3D segments that is robust to environment changes and does not depend on perfect segmentation or object presence.

Findings

Achieves 1Hz localization accuracy on KITTI dataset

Reliable loop detection and closure in real-time

Effective in large-scale, unstructured environments

Abstract

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.