OSK: A Novel LiDAR Occupancy Set Key-Based Place Recognition Method in Urban Environment

Light detection and ranging (LiDAR)-based place recognition plays a crucial role in autonomous driving and robotics systems. The feature information within LiDAR point clouds collected in urban environments is often sparse and scattered, which brings challenges to capture the essential feature by structured descriptors like matrices. In this article, a novel descriptor called Occupancy Set is designed for encoding urban environments by introducing a set-based descriptor, based on which a new place recognition method is proposed. Specifically, an occupancy set is populated with ring and sector indices representing the occupied regions around selected landmarks, and the proportion of identical elements in occupancy sets can reflect the probability of them belonging to the same landmark. To expedite the retrieval of the same landmark, the occupancy set is further compressed into hash values, named as the occupancy set key (OSK), using a locality-sensitive hashing (LSH) algorithm, and the historically associated landmarks corresponding to the query scan can be quickly located by searching for OSKs in inverse dictionaries. The experimental comparisons between the proposed method and existing state-of-the-art algorithms are conducted on public KITTI and KITTI360 datasets, and the experimental results exhibit that the proposed method can operate in real-time and has better recall rates than existing methods, particularly in challenging scenarios, such as the intersections with varying paths. The codes of this article will be open source for readers at
https://github.com/ZhangZh3ng/Occupancy-Set-Key
.