ROSS: Radar Off-road Semantic Segmentation
This addresses the problem of autonomous navigation in off-road environments for robotics or vehicle systems, but it is incremental as it adapts existing methods to new data types.
The study tackled semantic segmentation in RADAR data for off-road environments by developing a pipeline that uses LIDAR data and an existing annotated dataset to generate RADAR labels, representing RADAR data as images, and validated it with real-world datasets to highlight RADAR's potential for navigation.
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for generating RADAR labels, in which the RADAR data are represented as images. Validated with real-world datasets, our pragmatic approach underscores the potential of RADAR technology for navigation applications in off-road environments.