SPR: Single-Scan Radar Place Recognition

Localization is a crucial component for the navigation of autonomous vehicles. It encompasses global localization and place recognition, allowing a system to identify locations that have been mapped or visited before. Place recognition is commonly approached using cameras or LiDARs. However, these sensors are affected by bad weather or low lighting conditions. In this paper, we exploit automotive radars to address the problem of localizing a vehicle within a map using single radar scans. The effectiveness of radars is not dependent on environmental conditions, and they provide additional information not present in LiDARs such as Doppler velocity and radar cross section. However, the sparse and noisy radar measurement makes place recognition a challenge. Recent research in automotive radars addresses the sensor's limitations by aggregating multiple radar scans and using high-dimensional scene representations. We, in contrast, propose a novel neural network architecture that focuses on each point of single radar scans, without relying on an additional odometry input for scan aggregation. We extract pointwise local and global features, resulting in a compact scene descriptor vector. Our model improves local feature extraction by estimating the importance of each point for place recognition and enhances the global descriptor by leveraging the radar cross section information provided by the sensor. We evaluate our model using nuScenes and the 4DRadarDataset, which involve 2D and 3D automotive radar sensors. Our findings illustrate that our approach achieves state-of-the-art results for single-scan place recognition using automotive radars.