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Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.

Jun-Hyuck Im1, Sung-Hyuck Im2, Gyu-In Jee3

  • 1Department of Electronic Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea. junhyuck@konkuk.ac.kr.

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A new vehicle localization method uses an Extended Line Map (ELM) created from 3D LIDAR data. This approach achieves precise positioning with a small data size and fast matching times, enabling accurate self-driving navigation.

Keywords:
3D LIDARextended line mapprecise vehicle localizationroad markingvertical structure

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Area of Science:

  • Robotics
  • Computer Vision
  • Geospatial Information Systems

Background:

  • Accurate vehicle localization is crucial for autonomous driving systems.
  • Existing methods often require large map data or struggle with real-time performance.

Purpose of the Study:

  • To propose and evaluate a precise vehicle localization method using an Extended Line Map (ELM).
  • To demonstrate the efficiency and accuracy of the ELM-based approach with 3D LIDAR data.

Main Methods:

  • Generated a binary occupancy grid map from 3D LIDAR reflectivity and distance data.
  • Detected lines using Hough transform and stored them as an ELM in node and link forms.
  • Performed localization via correlation matching between the ELM and current sensor data using Fast Fourier Transform (FFT).

Main Results:

  • The ELM data size was extremely small (134 KB/km).
  • Correlation matching using FFT achieved a localization time of approximately 78 ms.
  • Experimental results in Seoul showed root mean square (RMS) position errors of 0.136 m (lateral) and 0.223 m (longitudinal) over 4.2 km.

Conclusions:

  • The proposed ELM-based vehicle localization method offers high precision and efficiency.
  • The method is suitable for real-time applications in autonomous driving, even at high speeds.