Prof. Ing. Adam Herout, Ph.D.

VEĽAS Martin, ŠPANĚL Michal, HRADIŠ Michal and HEROUT Adam. CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data. In: Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions. Torres Vedras, 2018, pp. 1-1.
Publication language:english
Original title:CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
Title (cs):Využití konvolučních sítí pro velmi rychlou segmentaci země v datech LiDARu Velodyne
Pages:1-1
Proceedings:Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions
Conference:IEEE International Conference on Autonomous Robot Systems and Competitions
Place:Torres Vedras, PT
Year:2018
DOI:10.1109/ICARSC.2018.8374167
Keywords
convolutional neural networks, ground segmentation, Velodyne, LiDAR
Annotation
This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose framework is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis indexes channels (i.e. laser beams). We proposed multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) and evaluated them using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.
BibTeX:
@INPROCEEDINGS{
   author = {Martin Ve{\'{l}}as and Michal {\v{S}}pan{\v{e}}l
	and Michal Hradi{\v{s}} and Adam Herout},
   title = {CNN for Very Fast Ground Segmentation in Velodyne
	LiDAR Data},
   pages = {1--1},
   booktitle = {Proceedings of IEEE International Conference on Autonomous
	Robot Systems and Competitions},
   year = {2018},
   location = {Torres Vedras, PT},
   doi = {10.1109/ICARSC.2018.8374167},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11346}
}

Your IPv4 address: 107.23.129.77
Switch to https