Keywords

3D Computer Vision, LiDAR Perception, Deep Learning, Autonomous Driving

Abstract

In recent years, LiDAR has emerged as a crucial perception tool for robotics and autonomous vehicles. However, most LiDAR perception methods are adapted from 2D image-based deep learning methods, which are not well-suited to the unique geometric structure of LiDAR point cloud data. This domain gap poses challenges for the fast-growing LiDAR perception tasks. This dissertation aims to investigate suitable deep network structures tailored for LiDAR point cloud data, and therefore design a more efficient and robust LiDAR perception framework. Our approach to address this challenge is twofold. First, we recognize that LiDAR point cloud data is characterized by an imbalanced and sparse distribution in the 3D space, which is not effectively captured by traditional voxel-based convolution methods that treat the 3D map uniformly. To address this issue, we aim to develop a more efficient feature extraction method by either counteracting the imbalanced feature distribution or incorporating global contextual information using a transformer decoder. Second, besides the gap between the 2D and 3D domains, we acknowledge that different LiDAR perception tasks have unique requirements and therefore require separate network designs, resulting in significant network redundancy. To address this, we aim to improve the efficiency of the network design by developing a unified multi-task network that shares the feature-extracting stage and performs different tasks using specific heads. More importantly, we aim to enhance the accuracy of different tasks by leveraging the multi-task learning framework to enable mutual improvements. We propose different models based on these motivations and evaluate them on several large-scale LiDAR point cloud perception datasets, achieving state-of-the-art performance. Lastly, we summarize the key findings of this dissertation and propose future research directions.

Completion Date

2023

Semester

Fall

Committee Chair

Foroosh, Hassan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Compute Science

Format

application/pdf

Identifier

DP0028106

URL

https://purls.library.ucf.edu/go/DP0028106

Language

English

Release Date

December 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Campus Location

Orlando (Main) Campus

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