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

arXiv: https://arxiv.org/abs/2206.12628 · HTML: https://mlsanity.com/paper/2206.12628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2022.

## Built on by (most cited first, 2 of 2)

- [Calibrated Keyframe-Segment Selection for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments](https://mlsanity.com/paper/2512.09447/) (2025)
- [UpDown-SC: Gravity-Canonicalized Dual-Envelope Scan Context for Indoor LiDAR Place Recognition](https://mlsanity.com/paper/2609.29118/) (2026)

## Abstract (arXiv)

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.