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Hauler Detection for an Autonomous Wheel Loader
Örebro University, School of Science and Technology, Örebro University, Sweden.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis work, we evaluate an object recognition system for an autonomous

wheel loader, to detect objects in its vicinity, in particular an articulated hauler

truck, by using an interest point extraction method that explicitly considers object

borders information, combined with a feature descriptor known as Normal

Aligned Radial Features (NARF) in 3D point cloud data. The object recognition

technique relies on extraction of NARF from range images (computed from

point clouds) for both model(hauler) and the scene. The technique used is robust

feature matching where the extracted model features are mapped on to

the scene containing the model and then seeking for a best transformation that

aligns the model with respect to the scene.

In this context we conducted several experiments with many number of 3D

scans obtained from the laser scanner mounted on the top of an autonomous

wheel loader to analyze the accuracy of the object recognition system. Finally

we demonstrated the results, as the system is able to recognize the hauler from

any view point.


Place, publisher, year, edition, pages
National Category
Computer Engineering
URN: urn:nbn:se:oru:diva-26405ISRN: ORU-NAT/DAT-AS-2012/0009--SEOAI: diva2:566813
Subject / course
Computer Engineering
Available from: 2012-11-07 Created: 2012-11-09 Last updated: 2013-10-22Bibliographically approved

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School of Science and Technology, Örebro University, Sweden
Computer Engineering

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