Object Detection and Classification Based on Point Separation Distance Features of Point Cloud Data

Date of Award

2023

Degree Name

M.S. in Electro-Optics

Department

Department of Electro-Optics and Photonics.

Advisor/Chair

Edward Watson

Abstract

Today, with the development of artificial intelligence and autonomous driving in full swing, lidar is playing a vital role. As an important sensing and detection component, lidar uses 3D point cloud images as a medium to allow artificial intelligence systems to perceive the outside world and perform reasoning work. Therefore, the processing and operation implementation of point cloud is an important part of the information processing of a lidar system, which will determine the accuracy and feasibility of artificial intelligence judgment. In this thesis, an analysis method based on extracting point cloud point separation distance distribution features is used. First, we will introduce how a lidar system works and how a lidar system collects information and generates a 3D point cloud. Afterward, feature analysis of point cloud point separation distribution for dimensionality reduction will be proposed. At the same time, we will use the point separation distribution feature to do object classification, object recognition and segmentation of whether there are vehicles on the road. What's more worth mentioning is that we also provide deep learning results and analysis based on point cloud point separation distribution features. On this basis, we discuss the significance and practicality of this feature analysis.

Keywords

point cloud, point separation distance features, Lidar, object detection and classification, deep learning

Rights Statement

Copyright © 2023, Author

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