Online Point Cloud Object Recognition System using Local Descriptors for Real-time Applications

Yacine Yaddaden, Yacine Yaddaden, Sylvie Daniel, Sylvie Daniel, Denis Laurendeau, Denis Laurendeau

Abstract

In the context of vehicle localization based on point cloud data collected using LiDAR sensors, several 3D descriptors might be employed to highlight the relevant information about the vehicle’s environment. However, it is still a challenging task to assess which one is the more suitable with respect to the constraint of real-time processing. In this paper, we propose a system based on classical machine learning techniques and performing recognition from point cloud data after applying several preprocessing steps. We compare the performance of two distinct state-of-the-art local 3D descriptors namely Unique Shape Context and Signature of Histograms of Orientation when combined with online learning algorithms. The proposed system also includes two distinct modes namely normal and cluster to deal with the point cloud data size and for which performances are evaluated. In order to measure the performance of the proposed system, we used a benchmark RGB-D object dataset from which we randomly selected three stratified subsets. The obtained results are promising and suggesting further experimentation involving real data collected from LiDAR sensors on vehicles.

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Paper Citation


in Harvard Style

Yaddaden Y., Daniel S. and Laurendeau D. (2021). Online Point Cloud Object Recognition System using Local Descriptors for Real-time Applications.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 301-308. DOI: 10.5220/0010198703010308


in Bibtex Style

@conference{visapp21,
author={Yacine Yaddaden and Sylvie Daniel and Denis Laurendeau},
title={Online Point Cloud Object Recognition System using Local Descriptors for Real-time Applications},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010198703010308},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Online Point Cloud Object Recognition System using Local Descriptors for Real-time Applications
SN - 978-989-758-488-6
AU - Yaddaden Y.
AU - Daniel S.
AU - Laurendeau D.
PY - 2021
SP - 301
EP - 308
DO - 10.5220/0010198703010308