RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold

TL;DR

This paper introduces RaPlace, a radar-only place recognition method using Radon transform and mutable threshold to achieve robust, transform-invariant localization despite noise and environmental variability, with efficient hierarchical retrieval.

Contribution

RaPlace is the first to combine Radon transform, cross-correlation, and mutable threshold for robust radar place recognition with reduced computational complexity.

Findings

Achieves high accuracy in intra-session loop-closure detection.

Demonstrates reliable global place recognition on public datasets.

Outperforms existing radar place recognition methods.

Abstract

Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance during place recognition, while ignoring the effects of radar multipath and ring noises. In addition, we compute the radar similarity distance using mutable threshold to mitigate variability of the similarity score, and reduce the time complexity of processing a copious radar data with hierarchical retrieval. We demonstrate the matching performance for both intra-session loop-closure detection and global place recognition using a publicly available imaging radar datasets. We verify reliable performance compared to existing stable radar place recognition method. Furthermore, codes for the proposed imaging radar place recognition is released for community.