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An Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Models
dc.contributor.author | Yigzaw, Samuel | |
dc.date.accessioned | 2023-01-11 19:53:31 (GMT) | |
dc.date.available | 2023-01-11 19:53:31 (GMT) | |
dc.date.issued | 2023-01-11 | |
dc.date.submitted | 2022-12-14 | |
dc.identifier.uri | http://hdl.handle.net/10012/19050 | |
dc.description.abstract | Light 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.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | LiDAR | en |
dc.subject | object detection | en |
dc.subject | Autoware.AI | en |
dc.subject | OpenPCDet | en |
dc.title | An Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Models | en |
dc.type | Master Thesis | en |
dc.pending | false | |
uws-etd.degree.department | Electrical and Computer Engineering | en |
uws-etd.degree.discipline | Electrical and Computer Engineering | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.degree | Master of Applied Science | en |
uws-etd.embargo.terms | 0 | en |
uws.contributor.advisor | Fischmeister, Sebastian | |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |