Local Descriptor for Robust Place Recognition using LiDAR Intensity

TL;DR

This paper introduces ISHOT, a novel LiDAR descriptor combining geometric and intensity data, significantly improving place recognition robustness in challenging environments.

Contribution

The paper proposes a new LiDAR descriptor called ISHOT that integrates intensity information with geometry, enhancing place recognition performance.

Findings

ISHOT outperforms state-of-the-art geometric descriptors.

The probabilistic keypoint voting algorithm achieves sublinear recognition times.

Validated in large-scale unstructured environments.

Abstract

Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend to affect camera-based solutions significantly. Compared to cameras, however, LiDARs lack the strong and descriptive appearance information that imaging can provide. To combine the benefits of geometry and appearance, we propose coupling the conventional geometric information from the LiDAR with its calibrated intensity return. This strategy extracts extremely useful information in the form of a new descriptor design, coined ISHOT, outperforming popular state-of-art geometric-only descriptors by significant margin in our local descriptor evaluation. To complete the framework, we furthermore develop a probabilistic keypoint voting place recognition algorithm, leveraging the new descriptor and yielding sublinear place recognition performance. The efficacy of our approach is validated in challenging global localization experiments in large-scale built-up and unstructured environments.