Abstract
Road detection is an important task in environment perception of intelligent driving. The road detection method is usually affected by many aspects, such as the occlusion of road vehicles and pedestrians, the change of illumination, etc. It is noting that lidar is insensitive to light and perceive the 3D environment information around the vehicle well during the day and night. So lidar road detection is suitable for intelligent driving scenarios. In this work, a Graph Convolution Network, named GCN RoadNet, is proposed for road detection using lidar point cloud, which discriminates on-road points from off-road points. Graph convolution network focuses more on local connections of point cloud points than global ones, therefore it achieves better accuracy than PointNet. Experiments on the KITTI dataset validate the robustness and effectiveness of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
