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

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Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsAlain Tremeau, Jose Braz, Francisco Imai
Volume4
PublisherSCITEPRESS Digital Library
Publication date2018
Pages166-173
ISBN (Electronic)978-989-758-290-5
DOIs
Publication statusPublished - 2018
Event13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2018 - Funchal, Madeira, Portugal
Duration: 27. Jan 201829. Jan 2018
Conference number: 13
http://www.visapp.visigrapp.org/?y=2018

Conference

Conference13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Number13
CountryPortugal
CityFunchal, Madeira
Period27/01/201829/01/2018
Internet address

Keywords

  • Agricultural robot
  • Neural network
  • Sensor fusion

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