# GeoLCR: Attention-based Geometric Loop Closure and Registration

## TL;DR

GeoLCR introduces an attention-based geometric loop closure and registration algorithm utilizing lidar data, achieving high precision and perfect loop detection success across multiple datasets, advancing autonomous vehicle perception capabilities.

## Contribution

The paper presents a novel lidar-based loop detection and registration method with an innovative point-level registration model and attention mechanism, outperforming existing approaches in accuracy and success rate.

## Key findings

- Up to twofold increase in translation and rotation accuracy.
- Achieved 100% success rate in loop detection on challenging sequences.
- Validated across multiple datasets including KITTI and Nuscenes.

## Abstract

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.