CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU132245" target="_blank" >RIV/00216305:26230/18:PU132245 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374167" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8374167</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICARSC.2018.8374167" target="_blank" >10.1109/ICARSC.2018.8374167</a>
Alternative languages
Result language
angličtina
Original language name
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Conference on Autonomous Robot Systems and Competitions
ISBN
978-1-5386-5221-3
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
97-103
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
Torres Vedras
Event location
Torres Vedras
Event date
Apr 25, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000435384800018