Fast LIDAR-based road detection using fully convolutional neural networks
Paper i proceeding, 2017

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.

Benchmarking

Intelligent vehicle highway systems

Neural networks

Image coding

Convolution

Transportation

Semantics

Optical radar

Författare

Luca Caltagirone

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Samuel Scheidegger

Chalmers, Signaler och system

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Mattias Wahde

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

IEEE Intelligent Vehicles Symposium, Proceedings

1019-1024

Ämneskategorier

Datavetenskap (datalogi)

Datorsystem

Datorseende och robotik (autonoma system)

DOI

10.1109/IVS.2017.7995848

ISBN

978-150904804-5

Mer information

Senast uppdaterat

2018-03-16