Improving Camera-Based BEV Map Segmentation by Sampling Repeatedly

Semantic segmentation of the scene surrounding a car in Bird's-Eye-View (BEV) is an important task for the safe operation of an automated vehicle. Information about drivable areas and interactions with vulnerable road users such as pedestrians needs to be available. Most methods for local semantic scene segmentation rely on a combination of cameras with expensive lidar sensors for accurate mapping of the BEV scene. We propose a camera-only approach that aims to solve the map segmentation task by estimating ground height in the scene for resource efficient perspective view to BEV lifting.