Stereo and LIDAR fusion based detection of humans and other obstacles in farming scenarios

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Abstrakt

In this paper we propose a fusion method which uses the depth information acquired from a LIDAR sensor to guide a block matching stereo algorithm. The resulting fused point clouds are then used for obstacle detection, either by processing the raw data and clustering the protruding objects in the scene, or by applying a Convolutional Neural Network on the 3D points and labeling them into classes. The performance of the proposed method is evaluated by carrying out a series of experiments on different data sets obtained from the SAFE robotic platform. The results show that the fusion algorithm significantly improves the F1 detection score of the trained networks.

OriginalsprogEngelsk
TitelProceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
RedaktørerAlain Tremeau, Francisco Imai, Jose Braz
Vol/bind4
ForlagSCITEPRESS Digital Library
Publikationsdato2018
Sider166-173
ISBN (Elektronisk)978-989-758-290-5
DOI
StatusUdgivet - 2018
Begivenhed13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2018 - Funchal, Madeira, Portugal
Varighed: 27. jan. 201829. jan. 2018
Konferencens nummer: 13
http://www.visapp.visigrapp.org/?y=2018

Konference

Konference13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Nummer13
Land/OmrådePortugal
ByFunchal, Madeira
Periode27/01/201829/01/2018
Internetadresse

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