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Related Experiment Video

Updated: Jan 16, 2026

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Research on LiDAR-Assisted Optimization Algorithm for Terrain-Aided Navigation of eVTOL.

Guangming Zhang1, Jing Zhou1, Zhonghang Duan1

  • 1School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China.

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|September 27, 2025
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Summary

This study introduces a LiDAR-based terrain-matching algorithm for precise navigation of electric vertical take-off and landing (eVTOL) aircraft in GPS-denied urban areas. The new method significantly improves positioning accuracy and robustness in challenging environments.

Keywords:
LiDARdigital elevation modelelectric vertical takeoff and landing aircraftmulti-sensor fusionterrain-aided navigation

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Area of Science:

  • Robotics and Autonomous Systems
  • Geospatial Navigation
  • Aerospace Engineering

Background:

  • Urban low-altitude environments pose significant challenges for electric vertical take-off and landing (eVTOL) aircraft navigation due to Global Navigation Satellite System (GNSS) signal denial and complex terrain.
  • Inertial Navigation Systems (INS) suffer from accumulated errors, necessitating robust augmentation for precise positioning.

Purpose of the Study:

  • To develop a high-precision navigation algorithm for eVTOLs in GNSS-denied urban environments.
  • To leverage Light Detection and Ranging (LiDAR) data for terrain-based navigation and correction of INS errors.

Main Methods:

  • A novel terrain-matching optimization algorithm utilizing LiDAR point cloud data to construct a Digital Elevation Model (DEM).
  • Incorporation of enhanced feature description for critical terrain regions.
  • Implementation of an adaptive Random Sample Consensus (RANSAC)-based mechanism for misalignment detection and INS error correction.

Main Results:

  • The proposed algorithm achieved positioning accuracy better than 2 meters in complex urban canyon simulations.
  • Demonstrated a 25.0% and 31.4% improvement in accuracy compared to traditional SIFT-RANSAC and SURF-RANSAC methods, respectively.
  • Achieved a feature matching accuracy rate of 90.4% and a localization success rate of 96.8% at a 95% confidence level.

Conclusions:

  • The LiDAR-based terrain-matching algorithm significantly enhances the navigation and localization accuracy and robustness of eVTOLs in challenging low-altitude urban settings.
  • This approach provides a viable solution for precise eVTOL operations where GNSS is unreliable.