Publication Details
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
Španěl Michal, Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
convolutional neural networks, ground segmentation, Velodyne, LiDAR
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 approach 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 represents channels - laser beams. Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated, 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.
@inproceedings{BUT157178,
author="Martin {Veľas} and Michal {Španěl} and Michal {Hradiš} and Adam {Herout}",
title="CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data",
booktitle="IEEE International Conference on Autonomous Robot Systems and Competitions",
year="2018",
pages="97--103",
publisher="Institute of Electrical and Electronics Engineers",
address="Torres Vedras",
doi="10.1109/ICARSC.2018.8374167",
isbn="978-1-5386-5221-3",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374167"
}