FastLCD: A fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping

In simultaneous localization and mapping (SLAM), loop closure detection is a significant yet still open problem. It contributes to construct a globally consistent and accurate map. This paper proposes a fast and compact loop closure detection method (FastLCD) based on comprehensive descriptors and machine learning to achieve reliable and precise results using 3D point cloud for indoor LiDAR mobile mapping. Comprehensive descriptors proposed in this paper encode discriminative multimodality features to describe each scan of point clouds. The specific values of descriptors of point cloud scan pairs are fed into a machine learning model. We leverage the pre-trained learning model as a classifier to distinguish whether a pair of laser scans is a loop candidate. Then, to ensure the results' precision, a novel double-deck loop candidate verification strategy is used to reject false positives. The algorithm is evaluated on datasets of some typical indoor environments. Compared with some state-of-the-art loop closure detection algorithms, the proposed FastLCD algorithm demonstrates superior performance in precision and recall rate. Moreover, the method proposed also exhibits high time efficiency, excellent generalization performance and insensitivity to threshold changes.

Automated summary

In this paper, a fast and compact loop closure detection method is proposed based on comprehensive descriptors and machine learning for indoor LiDAR mobile mapping, achieving reliable and precise results. By feeding specific descriptor values into a machine learning model and using a loop candidate verification strategy, the proposed method shows superior performance in precision and recall rate.