mmPlace: Robust Place Recognition with Intermediate Frequency Signal of Low-cost Single-chip Millimeter Wave Radar

TL;DR

mmPlace is a novel place recognition system using low-cost single-chip millimeter wave radar, transforming IF signals into heatmaps and employing rotation strategies to improve robustness in challenging environments.

Contribution

The paper introduces mmPlace, a new approach that converts radar signals into heatmaps and uses rotation-based techniques to enhance place recognition performance.

Findings

Achieves 87.37% recall@1 in challenging scenarios

Outperforms point cloud-based methods

Effective in environments with rotational and lateral variations

Abstract

Place recognition is crucial for tasks like loop-closure detection and re-localization. Single-chip millimeter wave radar (single-chip radar in short) emerges as a low-cost sensor option for place recognition, with the advantage of insensitivity to degraded visual environments. However, it encounters two challenges. Firstly, sparse point cloud from single-chip radar leads to poor performance when using current place recognition methods, which assume much denser data. Secondly, its performance significantly declines in scenarios involving rotational and lateral variations, due to limited overlap in its field of view (FOV). We propose mmPlace, a robust place recognition system to address these challenges. Specifically, mmPlace transforms intermediate frequency (IF) signal into range azimuth heatmap and employs a spatial encoder to extract features. Additionally, to improve the performance in scenarios involving rotational and lateral variations, mmPlace employs a rotating platform and concatenates heatmaps in a rotation cycle, effectively expanding the system's FOV. We evaluate mmPlace's performance on the milliSonic dataset, which is collected on the University of Science and Technology of China (USTC) campus, the city roads surrounding the campus, and an underground parking garage. The results demonstrate that mmPlace outperforms point cloud-based methods and achieves 87.37% recall@1 in scenarios involving rotational and lateral variations.