FreSCo: Frequency-Domain Scan Context for LiDAR-based Place Recognition with Translation and Rotation Invariance

TL;DR

FreSCo introduces a Fourier Transform-based global descriptor for LiDAR place recognition that achieves translation and rotation invariance, improving robustness and accuracy in urban environments.

Contribution

The paper presents FreSCo, a novel frequency-domain descriptor for LiDAR-based place recognition with invariance to viewpoint changes, along with a fast pose estimation method.

Findings

FreSCo outperforms existing methods on multiple datasets.

It achieves translation and rotation invariance in place recognition.

The approach is computationally efficient and robust in urban scenes.

Abstract

Place recognition plays a crucial role in re-localization and loop closure detection tasks for robots and vehicles. This paper seeks a well-defined global descriptor for LiDAR-based place recognition. Compared to local descriptors, global descriptors show remarkable performance in urban road scenes but are usually viewpoint-dependent. To this end, we propose a simple yet robust global descriptor dubbed FreSCo that decomposes the viewpoint difference during revisit and achieves both translation and rotation invariance by leveraging Fourier Transform and circular shift technique. Besides, a fast two-stage pose estimation method is proposed to estimate the relative pose after place retrieval by utilizing the compact 2D point clouds extracted from the original data. Experiments show that FreSCo exhibited superior performance than contemporaneous methods on sequences of different scenes from multiple datasets. Code will be publicly available at https://github.com/soytony/FreSCo.