GeoLCR: Attention-based Geometric Loop Closure and Registration
Liang, Jing, Son, Sanghyun, Lin, Ming, Manocha, Dinesh
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jul-16-2023
- Country:
- North America > United States > Maryland (0.14)
- Genre:
- Research Report > Promising Solution (0.46)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning (1.00)
- Robots (1.00)
- Vision (1.00)
- Information Technology > Artificial Intelligence