TransLoc4D: Transformer-Based 4D Radar Place Recognition

Abstract

Place recognition is crucial for unmanned vehicles in terms of localization and mapping. Recent years have witnessed numerous explorations in the field, where 2D cameras and 3D LiDARs are mostly employed. Despite their admirable performance, they may encounter challenges in adverse weather such as rain and fog. Hopefully, 4D millimeter-wave radar emerges as a promising alternative, as its longer wavelength makes it virtually immune to interference from tiny particles of fog and rain. Therefore, in this work, we propose a novel 4D radar place recognition model, TransLoc4D, based on sparse convolutions and Transformer structures. Specifically, a MinkLoc4D back-bone is first proposed to leverage the multimodal information from 4D radar scans. Rather than merely capturing geometric structures of point clouds, MinkLoc4D additionally explores their intensity and velocity properties. After feature extraction, a Transformer layer is introduced to enhance local features before aggregation, where linear self-attention captures the long-range dependencies of the point cloud, alleviating its sparsity and noise. To validate TransLoc4D, we construct two datasets and set up benchmarks for 4D radar place recognition. Experiments vali-date the feasibility of TransLoc4D and demonstrate it can robustly deal with dynamic and adverse environments.