Publication detail

CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

VEĽAS, M. ŠPANĚL, M. HRADIŠ, M. HEROUT, A.

Original Title

CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

English Title

CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

convolutional neural networks, ground segmentation, Velodyne, LiDAR

Released

27.04.2018

Publisher

Institute of Electrical and Electronics Engineers

Location

Torres Vedras

ISBN

978-1-5386-5221-3

Book

IEEE International Conference on Autonomous Robot Systems and Competitions

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

97

Pages to

103

Pages count

7

URL

Documents

BibTex


@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",
  annote="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.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="IEEE International Conference on Autonomous Robot Systems and Competitions",
  chapter="157178",
  doi="10.1109/ICARSC.2018.8374167",
  edition="NEUVEDEN",
  howpublished="online",
  institution="Institute of Electrical and Electronics Engineers",
  year="2018",
  month="april",
  pages="97--103",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}