[RAL 2024] Narrowing your FOV with SOLiD Descriptor for FOV-constrained LiDAR Place Recognition

Title: Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor for FOV-constrained LiDAR Place Recognition

Abstract: We often encounter limited FOV situations due to various factors such as sensor fusion or sensor mount in real-world robot navigation. However, the limited FOV interrupts the generation of descriptions and impacts place recognition adversely. Therefore, we suffer from correcting accumulated drift errors in a consistent map using LiDAR-based place recognition with limited FOV. Thus, in this paper, we propose a robust LiDAR-based place recognition method for handling narrow FOV scenarios. The proposed method establishes spatial organization based on the range-elevation bin and azimuth-elevation bin to represent places. In addition, we achieve a robust place description through reweighting based on vertical direction information. Based on these representations, our method enables addressing rotational changes and determining the initial heading. Additionally, we designed a lightweight and fast approach for the robot's onboard autonomy. For rigorous validation, the proposed method was tested across various LiDAR place recognition scenarios (i.e., single-session, multi-session, and multi-robot scenarios). To the best of our knowledge, we report the first method to cope with the restricted FOV.

Paper: https://ieeexplore.ieee.org/abstract/...
Github : https://github.com/sparolab/solid
Project page: https://sites.google.com/view/lidar-s...

Reference:
Hogyun Kim, Jiwon Choi, Taehu Sim, Giseop Kim, and Younggun Cho,
"Narrowing your FOV with SOLiD: Spatially Organized and Lightweight Global Descriptor forFOV-constrained LiDAR Place Recognition,"
IEEE Robotics and Automation Letters, 2024