An Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Models
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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.
Cite this version of the work
Samuel Yigzaw (2023). An Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Models. UWSpace. http://hdl.handle.net/10012/19050