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SiaN-VO: Siamese Network for Visual Odometry.

Bruno S Faiçal¹, Cesar A C Marcondes¹, Filipe A N Verri¹

¹Computer Science Division, Aeronautics Institute of Technology-ITA, Sao Jose dos Campos 12228-900, SP, Brazil.

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|February 10, 2024

Summary

This study enhances SiaN-VO, a drone navigation system, to improve visual odometry and displacement predictions during dynamic flights. The improved system offers more reliable drone navigation despite GPS signal loss.

Keywords:

autonomous flight drone visual odometry

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Area of Science:

Robotics and Automation
Computer Vision
Aerospace Engineering

Background:

Drone navigation relies heavily on sensor data integrity, with Global Navigation Satellite Systems (GNSS) being vulnerable to jamming and signal loss.
Existing visual odometry methods struggle with the complexities of dynamic flight conditions, impacting the reliability of drone flight plans.

Purpose of the Study:

To enhance the SiaN-VO (Siamese neural network for visual odometry) system for improved drone navigation.
To enable accurate displacement predictions in dynamic flight conditions by integrating multi-sensor data.
To address the limitations of GNSS-dependent navigation in challenging environments.

Main Methods:

Development of an enhanced Siamese neural network architecture (SiaN-VO).
Integration of data from multiple sensors beyond visual odometry.
Testing and validation in simulated and real-world dynamic flight scenarios.

Main Results:

The enhanced SiaN-VO demonstrates improved accuracy in displacement prediction during dynamic flights.
Multi-sensor data integration significantly boosts the robustness of the navigation system.
Preliminary results show promising performance in scenarios with intermittent GNSS availability.

Conclusions:

The enhanced SiaN-VO represents a significant advancement in drone navigation technology.
The system offers a more reliable alternative for drone navigation, especially when GNSS is compromised.
Further research will focus on real-time implementation and broader flight condition testing.