Abstract
In this article, we propose a road detection method based on the fusion of Lidar and image data under the framework of conditional random field. Firstly, Lidar point clouds are projected into the m...
Highlights
Road detection is always a key problem in autonomous driving
All the data are divided into three different categories and each category belongs to one road scenes: urban marked (UM), urban multiple marked (UMM) lanes, and urban unmarked (UU)
We propose a road detection method based on light detection and ranging (Lidar) and image data
Summary
Road detection is always a key problem in autonomous driving. For its driving safety, an unmanned ground vehicle must have the ability to distinguish road or free space from obstacles in various environments, as well as to obey the traffic rules. Due to the complex surroundings and obstacles in different environments, as well as various lighting conditions, road detection is still a challenging task. Many road detection methods based on monocular images have been proposed. With the rapid development of the 3-D sensors, more and more road detection methods are based on the stereo vision or laser range finder. In the outdoor scenes, the most common kind of 3-D sensors is the light detection and ranging (Lidar), such as the Velodyne HDL-64E (Velodyne LIDAR, USA). This kind of sensors can provide 3-D range data in the form of point clouds, which have maximum ranges of 60–80 m
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