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Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring.

Eunji Song¹, Seyoung Jeong¹, Sung-Ho Hwang¹

¹Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This study introduces a faster, more accurate method for autonomous driving object recognition using LiDAR ring data. The novel approach enhances detection performance in high-speed scenarios, outperforming existing methods.

Keywords:

3D LiDAR autonomous driving edge-triggered object detection rule-based

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision
Sensor Data Processing

Background:

High-speed autonomous driving demands rapid and precise object recognition, including small objects.
Existing methods face challenges in achieving both speed and accuracy in dynamic environments.

Purpose of the Study:

To develop an efficient object point extraction method for autonomous driving.
To improve the speed and performance of object recognition in high-speed situations using LiDAR data.

Main Methods:

A novel approach using rule-based LiDAR ring data and edge triggers for object point extraction.
Interpreting LiDAR ring information as digital pulses to effectively remove ground points.
Detecting discontinuous z-value edges aligned with ring ID and azimuth for object point identification.
Utilizing DBSCAN and PCA for bounding box creation and recognition performance assessment.

Main Results:

The proposed method demonstrated superior F1 scores compared to RANSAC on SemanticKITTI and Waymo Open Dataset for ground removal and point extraction.
Object bounding box extraction using the new method achieved higher PDR index performance.
Validation across open datasets, virtual, and real-world driving environments confirmed the method's effectiveness.

Conclusions:

The proposed LiDAR-based object point extraction method significantly enhances speed and performance in autonomous driving recognition.
This technique offers a robust solution for accurate object detection, even for small objects, in high-speed driving conditions.
The method shows strong potential for real-world implementation in advanced driver-assistance systems and fully autonomous vehicles.