Road Detection through CRF based LiDAR-Camera Fusion

In this paper, we propose a road detection method with LiDAR-camera fusion in a novel conditional random field (CRF) framework to exploit both range and color information. In the LiDAR based part, a fast height-difference based scanning strategy is applied in the 2D LiDAR range-image domain and a dense road detection result in camera image domain can be obtained through geometric upsampling given the LiDAR-camera calibration parameters. In the camera based part, a fully convolutional network is applied in the camera image domain. Finally, we fuse the dense and binary road detection results from both LiDAR and camera in a single CRF framework. Experiments show that using a single thread of CPU, the proposed LiDAR based part can operate at a frequency of over 250Hz with sparse output in range image and 40Hz with dense result in camera image for the 64-beam Velodyne scanner. Our CRF fusion method achieves very promising road detection performance on the KITTI-Road dataset.