Abstract
In this paper, a free space detection approach is proposed for the detection of drivable area in the context of autonomous driving. This approach aims to detect free space using only 3D LIDAR data as input, by organizing the LIDAR measurements in their sensor specific layer/channel representation. The unstructured 3D point cloud is converted into 2D panoramic images containing 3D coordinates related features. This representation allows employing semantic segmentation convolutional neural networks (CNNs) for the detection of the free space. Using CNNs, these images are semantically segmented into two classes: road and non-road. The final output is the free space region, that can be used for potential driving assistance functions. The proposed approaches are evaluated on a manually annotated set built from the KITTI road benchmark.
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.
