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Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas.

Kyu-Won Kim1, Gyu-In Jee1

  • 1Department of Electrical and Electronic Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea.

Sensors (Basel, Switzerland)
|February 28, 2020
PubMed
Summary

We developed a new Free-Resolution Probability Distributions Map (FRPDM) for precise vehicle localization using 3D LIDAR. This efficient map representation significantly reduces data size while improving accuracy in complex urban environments.

Keywords:
3D LIDARfree-resolution probability distributions map (FRPDM)precise vehicle localizationroad markingurban areavertical structure

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Area of Science:

  • Robotics
  • Computer Vision
  • Geospatial Analysis

Background:

  • Accurate vehicle localization is crucial for autonomous driving and advanced driver-assistance systems.
  • Existing probability distribution maps often face limitations in resolution flexibility and data storage efficiency.
  • 3D Light Detection and Ranging (LIDAR) provides rich environmental data for localization.

Purpose of the Study:

  • To propose a novel Free-Resolution Probability Distributions Map (FRPDM) for enhanced vehicle localization.
  • To develop an FRPDM-based precise localization method using 3D LIDAR data.
  • To evaluate the efficiency and accuracy of the proposed method in a complex urban setting.

Main Methods:

  • FRPDM generation using Gaussian mixture modeling on road markings and vertical structures from 3D LIDAR point clouds.
  • FRPDM-based localization employing point-to-probability distribution scan matching and feature-point matching.
  • Vehicle position and heading estimation using an extended Kalman filter-based navigation filter.

Main Results:

  • The proposed FRPDM achieved a very small map size (61 KB/km) due to efficient object representation.
  • Experimental results in Seoul's Gangnam area demonstrated high localization accuracy.
  • Root Mean Square (RMS) position errors were 0.057 m (lateral) and 0.178 m (longitudinal).

Conclusions:

  • The FRPDM offers an efficient and effective representation for 3D LIDAR-based mapping.
  • The FRPDM-based localization method achieves precise vehicle positioning and heading estimation.
  • The approach shows significant promise for real-world autonomous navigation applications.