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High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.

Veli Ilci1, Charles Toth2

  • 1Department of Geomatics Engineering, Ondokuz Mayis University, 55139 Atakum, Turkey.

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Summary

Autonomous vehicle sensors can create high-definition maps using only Velodyne sensor data. This study demonstrates centimeter-level accuracy for point cloud generation, proving the potential of these systems for mapping applications.

Keywords:
LiDARautonomous vehiclemobile mappingpoint cloudsensor fusion

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Area of Science:

  • Robotics and Autonomous Systems
  • Geomatics Engineering
  • Sensor Fusion

Background:

  • Autonomous systems rely on accurate environmental perception using diverse sensors like GNSS, IMU, LiDAR, radar, and cameras.
  • Rigorous sensor modeling is crucial to mitigate inherent error sources for reliable integrated solutions.
  • Mobile mapping systems and autonomous vehicles share environmental reconstruction goals but differ in sensor grade and operational objectives.

Purpose of the Study:

  • To evaluate the mapping performance of autonomous vehicle sensor systems.
  • To assess the potential of using only Velodyne sensor data for creating accurate point clouds.
  • To determine if centimeter-level accuracy is achievable with this sensor configuration.

Main Methods:

  • Focused solely on Velodyne sensor data for point cloud generation.
  • Assessed the performance potential of autonomous vehicle sensor systems.
  • No other sensor data or fusion techniques were employed in this specific investigation.

Main Results:

  • Velodyne sensor data alone is sufficient for generating accurate point clouds.
  • Achieved centimeter-level accuracy in the generated point clouds.
  • Demonstrated the high-definition mapping potential of autonomous vehicle sensors.

Conclusions:

  • Autonomous vehicle sensor systems, specifically Velodyne, possess significant potential for high-definition mapping.
  • Centimeter-level accuracy is attainable using only Velodyne sensor data, validating its utility for precise environmental reconstruction.
  • This research highlights the capability of cost-effective sensors in autonomous vehicles for advanced mapping tasks.