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Integrated Pose Estimation Using 2D Lidar and INS Based on Hybrid Scan Matching.

Gwangsoo Park1, Byungjin Lee1, Sangkyung Sung1

  • 1Department of Aerospace Information Engineering, Konkuk University, Seoul 05029, Korea.

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Summary

This study introduces a novel pose estimation method using 2D lidar feature points within the NDT framework. The approach enhances point cloud registration for aerial mobility systems, improving accuracy and computational efficiency.

Keywords:
localizationnormal distribution transformpose estimationregistrationscan matching

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Point cloud data is crucial for urban mobility, with 3D lidar and depth sensors excelling in mapping and localization.
  • 2D lidar is vital for resource-constrained systems like aerial mobility, requiring efficient pose estimation.

Purpose of the Study:

  • To propose a new pose estimation scheme leveraging 2D lidar feature points within the Normal Distributions Transform (NDT) framework.
  • To enhance point cloud registration for improved navigation in aerial mobility systems.

Main Methods:

  • Extracting vertices and corners as feature points from 2D lidar scans.
  • Functionalizing a point-to-point relationship and integrating it into a voxelized map matching process.
  • Combining the registration results with inertial navigation via an integration filter.

Main Results:

  • The proposed algorithm demonstrated improved accuracy and computational efficiency compared to previous techniques.
  • Verification through high-fidelity flight simulator and indoor experiments confirmed the algorithm's performance.
  • The method effectively utilizes 2D lidar feature points for enhanced point cloud registration.

Conclusions:

  • The developed pose estimation scheme offers a promising solution for resource-limited aerial mobility systems.
  • The integration of 2D lidar features into the NDT framework significantly boosts registration performance.
  • The algorithm achieves superior accuracy and computational efficiency in mobile object navigation.