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A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving.

Simegnew Yihunie Alaba¹, John E Ball¹

¹Department of Electrical and Computer Engineering, James Worth Bagley College of Engineering, Mississippi State University, Starkville, MS 39762, USA.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This survey reviews Light Detection and Ranging (LiDAR)-based 3D object detection and feature extraction for autonomous driving. It addresses challenges like data sparsity and occlusion, comparing state-of-the-art methods for enhanced perception.

Keywords:

3D object detection LiDAR autonomous vehicles classification deep learning deep learning for point cloud processing sparsity

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Area of Science:

Computer Vision
Robotics
Autonomous Systems

Background:

2D object detection lacks depth information crucial for autonomous driving.
LiDAR sensors provide essential 3D environmental data for robust perception.
Challenges in 3D object detection include data sparsity, scale variation, and occlusions.

Purpose of the Study:

To survey LiDAR-based 3D object detection techniques.
To review feature-extraction methods for LiDAR data.
To compare state-of-the-art 3D object detection approaches.

Main Methods:

Summarization of commonly used 3D coordinate systems.
Review of LiDAR-based 3D object detection methods.
Analysis of feature-extraction techniques for sparse LiDAR data.

Main Results:

Identified key challenges in LiDAR-based 3D object detection.
Presented a comparative overview of current state-of-the-art methods.
Highlighted the importance of 3D data for accurate environmental understanding.

Conclusions:

LiDAR is vital for autonomous driving perception systems.
Addressing data sparsity and other challenges is crucial for advancing 3D object detection.
This survey provides a comprehensive resource for LiDAR-based 3D perception research.