Abstract-In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.
Luca et al. in REF project LiDAR point to a top-view to create grid maps, with which a FCN can be used to perform fast pixel-wise road detection.
Fast LIDAR-based road detection using fully convolutional neural networks
2017 IEEE Intelligent Vehicles Symposium (IV)
2017 IEEE Intelligent Vehicles Symposium (IV)
Computer Science