Low-Level Radar-based Semantic Road Segmentation for Ground Robots

Abstract

Radar-based perception has been gaining traction in recent years, supported by improvements in deep learning techniques. Low-level radar perception focuses on utilizing the denser radar signal data instead of the conventional point-cloud. Despite the recent focus on this data representation, a lack of public datasets has limited the scope of research, especially for scene segmentation. In this paper, we address this challenge by recording a novel low-level radar dataset that includes diverse environments, sensors and complex scenarios. We propose Swin-FFM, a Swin transformer based network for free-road segmentation using the complex-valued range-Doppler signal. On our dataset, Swin-FFM achieves an IoU of 86.8%, demonstrating its ability to successfully output accurate free-road boundaries even in challenging settings. In addition to this, we compare our network with baselines for both low-level and point-cloud formats. Finally, we demonstrate the network’s ability to work with any low-level radar representation, highlighting its benefit for low-level radar perception.