We present a novel algorithm specially designed for loop detection and registration that utilizes Lidar-based perception. Our approach to loop detection involves voxelizing point clouds, followed by an overlap calculation to confirm whether a vehicle has completed a loop. We further enhance the current pose's accuracy via an innovative point-level registration model. The efficacy of our algorithm has been assessed across a range of well-known datasets, including KITTI, KITTI-360, Nuscenes, Complex Urban, NCLT, and MulRan. In comparative terms, our method exhibits up to a twofold increase in the precision of both translation and rotation estimations. Particularly noteworthy is our method's performance on challenging sequences where it outperforms others, being the first to achieve a perfect 100% success rate in loop detection.
@article{arxiv.2302.13509,
title = {GeoLCR: Attention-based Geometric Loop Closure and Registration},
author = {Jing Liang and Sanghyun Son and Ming Lin and Dinesh Manocha},
journal= {arXiv preprint arXiv:2302.13509},
year = {2023}
}