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Related Experiment Video

Updated: Jun 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving.

Seonghark Jeong¹, Heeseok Shin², Myeong-Jun Kim³

¹Propulsion Division, GM Korea Company, Incheon 21344, Republic of Korea.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study introduces an enhanced LiDAR mapping and localization system using YOLOv4 to remove dynamic objects, improving accuracy in GPS-denied urban areas. The Vision + LiDAR + NDT method significantly reduces localization errors, boosting autonomous navigation reliability.

Keywords:

DeepLabV3+LiDAR NDT YOLOv4 autonomous vehicle localization map matching semantic segmentation sensor fusion

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Area of Science:

Robotics and Autonomous Systems
Computer Vision
Sensor Fusion

Background:

Global Positioning System (GPS) localization is unreliable in urban environments due to signal obstruction.
Existing LiDAR-based mapping methods struggle with dynamic objects, leading to reduced accuracy.
Dynamic objects like vehicles degrade the performance of Simultaneous Localization and Mapping (SLAM) systems.

Purpose of the Study:

To develop an enhanced LiDAR-based mapping and localization system.
To improve localization accuracy and robustness in challenging urban environments.
To address the limitations of GPS and existing LiDAR segmentation methods in handling dynamic objects.

Main Methods:

Integration of YOLOv4 (You Only Look Once version 4) object detection with LiDAR data.
Utilizing YOLOv4 to detect and remove dynamic objects (e.g., vehicles) from sensor data.
Employing the enhanced sensor data with Normal Distributions Transform (NDT) for map matching and localization.

Main Results:

Significant improvements in map-matching and localization performance were observed, especially in urban settings.
Root Mean Square Error (RMSE) for localization decreased from 0.9870 to 0.9724 in open areas and 1.3874 to 1.1217 in urban areas.
The Vision + LiDAR + NDT approach demonstrated enhanced localization accuracy and reliability compared to conventional methods.

Conclusions:

The proposed system effectively enhances localization performance in both simple and complex environments.
By removing dynamic objects, the system improves the accuracy and robustness of autonomous navigation.
This GPS-independent approach offers a reliable solution for autonomous vehicles in high-traffic urban areas.