VCADNet: Vision-based Circular Accessible Depth Prediction for UGV Perception

Abstract

Circular accessible depth (CAD) provides a lightweight and robust traversability representation for autonomous navigation of unmanned ground vehicles (UGV). Aiming at the limitations of existing LiDAR-based methods in detecting low-thickness targets and executing semantic reasoning, we propose VCADNet, a vision-based neural network for circular accessible depth prediction. VCADNet comprises three core components: a geometry-based query module for multi-view bird’s eye view feature extraction, a polar coordinate transformation for CAD alignment, and a multi-scale U-Net architecture for depth prediction. In addition, we present a cross-modal contrastive learning scheme to enhance the spatial reasoning of VCADNet, which transfers knowledge from LiDAR-based encoders to vision-based counterparts. Extensive experiments demonstrate the superior performance of VCADNet in various UGV perception tasks.