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dc.contributor.authorYigzaw, Samuel
dc.date.accessioned2023-01-11 19:53:31 (GMT)
dc.date.available2023-01-11 19:53:31 (GMT)
dc.date.issued2023-01-11
dc.date.submitted2022-12-14
dc.identifier.urihttp://hdl.handle.net/10012/19050
dc.description.abstractLight Detection And Ranging (LiDAR) sensors are widely used in applications related to autonomous driving. The ability to scan and visualize the 3D surroundings of the vehicle as a point cloud is a particular strength of this sensor. Various different object detection models have been proposed to provide bounding box predictions given a point cloud. This thesis looks at two popular, open-source frameworks which provide solutions to this problem, Autoware.AI and OpenPCDet. The Autoware.AI framework provides models which use hand-crafted, non-neural network based methods to solve LiDAR-based object detection, while the OpenPCDet framework provides models based on neural networks. In this thesis, these models are compared with each other on a custom labeled dataset. As expected, the results of this comparison show that the non-neural network based Autoware.AI models perform significantly worse than the neural network based OpenPCDet models. Additionally, it is shown that amongst the OpenPCDet models, PV-RCNN performs better for detecting vehicles, SECOND and PV-RCNN perform better for detecting pedestrians, and SECOND and Part-A^2 Free perform better for detecting cyclists.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectLiDARen
dc.subjectobject detectionen
dc.subjectAutoware.AIen
dc.subjectOpenPCDeten
dc.titleAn Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Modelsen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentElectrical and Computer Engineeringen
uws-etd.degree.disciplineElectrical and Computer Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws-etd.embargo.terms0en
uws.contributor.advisorFischmeister, Sebastian
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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