Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

2017-03-10 · Luca Caltagirone, Samuel Scheidegger, Lennart Svensson, Mattias Wahde

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.

📄 PDF Abstract BibTeX arXiv:1703.03613

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Tasks

Semantic Segmentation

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