Pseudo-LiDAR Based Road Detection

Abstract

Road detection is a critically important task for self-driving cars. By employing LiDAR data, recent works have significantly improved the accuracy of road detection. Relying on LiDAR sensors limits the wide application of those methods when only cameras are available. In this paper, we propose a novel road detection approach with RGB being the only input during inference. Specifically, we exploit pseudo-LiDAR using depth estimation, and propose a feature fusion network where RGB and learned depth information are fused for improved road detection. To further optimize the network structure and improve the efficiency of the network. we search for the network structure of the feature fusion module using NAS techniques. Finally, be aware of that generating pseudo-LiDAR from RGB via depth estimation introduces extra computational costs and relies on depth estimation networks, we design a modality distillation strategy and leverage it to further free our network from these extra computational cost and dependencies during inference. The proposed method achieves state-of-the-art performance on two challenging benchmarks, KITTI and R2D.

13 pages, 11 figures. This paper has been accepted by TCSVT. IEEE Trans. Circuit Syst.Video Technol. 2022

References in corpus (5)

• Distilling the Knowledge in a Neural Network
• Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
• Progressive LiDAR Adaptation for Road Detection
• SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
• Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots