  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.
