Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

被引：0

作者：

Caltagirone, Luca ^{[1
]}

Scheidegger, Samuel ^{[2
,3
]}

Svensson, Lennart ^{[2
]}

Wahde, Mattias ^{[1
]}

机构：

[1] Chalmers Univ Technol, Appl Mech Dept, Adapt Syst Res Grp, Gothenburg, Sweden

[2] Chalmers Univ Technol, Signal & Syst Dept, Gothenburg, Sweden

[3] Autoliv Research, Stockholm, Sweden

来源：

2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) | 2017年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1019 / 1024

页数：6

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