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dc.contributor.authorQuadros, Alistair James
dc.date.accessioned2014-05-19
dc.date.available2014-05-19
dc.date.issued2013-10-17
dc.identifier.urihttp://hdl.handle.net/2123/10515
dc.description.abstractThis thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing.en_AU
dc.publisherUniversity of Sydney.en_AU
dc.publisherFaculty of Engineering & ITen_AU
dc.publisherSchool of Aerospace, Mechanical & Mechatronic Engineeringen_AU
dc.subjectLidaren_AU
dc.subject3Den_AU
dc.subjectFeaturesen_AU
dc.subjectRoboticsen_AU
dc.subjectKeypointsen_AU
dc.subjectVisionen_AU
dc.subjectObject classificationen_AU
dc.subjectVelodyneen_AU
dc.titleRepresenting 3D shape in sparse range images for urban object classificationen_AU
dc.typePhD Doctorateen_AU
dc.date.valid2014-01-01en_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU


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