SiaN-VO: Siamese Network for Visual Odometry

Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone fl...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-02, Vol.24 (3), p.973
Main Authors: Faiçal, Bruno S, Marcondes, Cesar A C, Verri, Filipe A N
Format: Article
Language:English
Subjects:
Accuracy
Aircraft
Altitude
autonomous flight
Datasets
drone
Embedded systems
Methods
Neural networks
Sensors
Unmanned aerial vehicles
Vehicles
visual odometry
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Summary:Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight plans. To address this, we introduce SiaN-VO, a Siamese neural network designed for visual odometry prediction in such challenging scenarios. Our preliminary studies have shown promising results, particularly for flights under static conditions (constant speed and altitude); while these findings are encouraging, they do not fully represent the complexities of real-world flight conditions. Therefore, in this paper, we have furthered our research to enhance SiaN-VO, improving data integration from multiple sensors and enabling more accurate displacement predictions in dynamic flight conditions, thereby marking a significant step forward in drone navigation technology.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24030973