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Part Detection in Oneline-Reconstructed 3D Models.
Örebro University, School of Science and Technology, Örebro University, Sweden.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis introduces a system to identify objects into a 3D reconstructed

model. In particular, this is applied to automatize the inspection of an engine

of a truck by detecting some parts in an online reconstructed 3D model. In this

way, the work shows how the use of the augmented reality and the computer

vision can be applied into a real application to automatize a task of inspection.

To do this, the system employs the Signed Distance Function for the 3D representation

which has been proven in other research as an efficient method for

3D reconstruction of environments. Then, some of the common processes for

the recognition of shapes are applied to identify the pose of a specific part of

the 3D model.

This thesis explains the steps to achieve this task. The model is built using

an industrial robot arm with a depth camera attached to the end effector. This

allows taking snapshots from different viewpoints that are fused in a same

frame to reconstruct the 3D model. The path for the robot is generated by

applying translations to the initial pose of the end effector. Once the model

is generated, the identification of the part is carried out. The reconstructed

model and the model to be detected are analysed by detecting keypoints and

features descriptors. These features can be computed together to obtain several

instances over the target model, in this case the engine. Last, these instances

can be filtered by the application of some constrains to get the true pose of the

object over the scene.

Last, some results are presented. The models were generated from a real

engine truck. Then, these models were analysed to detect the oil filters by using

different keypoint detectors. The results show that the quality of the recognition

is good for almost all of the cases but it still presents some failures for some

of the detectors. Keypoints too distinctive are more prune to produce wrong

registrations due to the differences between the target and the scene. At the

same time, more constrains make the detection more robust but also make the

system less flexible.

Place, publisher, year, edition, pages
2016. , 89 p.
National Category
Computer Science
URN: urn:nbn:se:oru:diva-51600OAI: diva2:951274
Subject / course
Computer Engineering
Available from: 2016-08-08 Created: 2016-08-08 Last updated: 2016-08-10Bibliographically approved

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