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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

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Verification of Vision-Based Terrain-Referenced Navigation Using the Iterative Closest Point Algorithm Through Flight

Taeyun Kim¹, Seongho Nam¹, Hyungsub Lee¹

¹4th R&D Institute, Agency for Defense Development, Daejeon 34060, Republic of Korea.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

This study introduces a robust vision-based terrain-referenced navigation (TRN) system using stereo imagery and a novel rotation-invariant ICP algorithm. The system enhances positioning accuracy and reliability, especially at high altitudes where GPS is limited.

Keywords:

iterative closest point algorithm stereo matching terrain-referenced navigation vision sensor

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Area of Science:

Robotics
Computer Vision
Aerospace Engineering

Background:

Global Positioning System (GPS) limitations necessitate alternative navigation methods.
Terrain-referenced navigation (TRN) offers a viable GPS-independent solution.
Existing TRN methods can be sensitive to sensor errors and environmental conditions.

Purpose of the Study:

To propose and validate a vision-based TRN framework using stereo imagery.
To enhance navigation robustness and accuracy, particularly at high altitudes.
To integrate a rotation-invariant Iterative Closest Point (ICP) algorithm for improved performance.

Main Methods:

Utilized stereo imagery to reconstruct 3D elevation maps.
Employed a rotation-invariant ICP algorithm to align reconstructed maps with a terrain database.
Conducted full-scale flight tests on a Cessna aircraft with integrated sensors (IMU, camera, altimeter).

Main Results:

The proposed vision-based TRN framework demonstrated enhanced positioning accuracy and robustness.
Performance improvements were particularly notable under challenging high-altitude conditions with reduced image resolution.
The algorithm maintained reliable performance across varying flight altitudes, outperforming a filter-based approach.

Conclusions:

The integration of rotation-invariant ICP with vision-based TRN is novel and effective.
The system provides a robust, GPS-independent navigation solution validated by real-world flight tests.
Findings have significant implications for unmanned aerial vehicles and long-range guided systems.