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. 2024 Feb 2;24(3):973.
doi: 10.3390/s24030973.

SiaN-VO: Siamese Network for Visual Odometry

Bruno S Faiçal  1 , Cesar A C Marcondes  1 , Filipe A N Verri  1
Affiliations

SiaN-VO: Siamese Network for Visual Odometry

Bruno S Faiçal et al. Sensors (Basel). .

Abstract

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.

Keywords: autonomous flight; drone; visual odometry.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Architecture of the Siamese Network for Visual Odometry (SiaN-VO).
Figure 2
Figure 2
Examples of images considering the environments in which the flights were simulated.
Figure 3
Figure 3
Example of expected routes (blue line) compared with routes that used prediction (red line) for 20% of the flight. The green dot signals the starting position of the flight.
Figure 4
Figure 4
Predicted and expected displacement values along the route Forest1.
See this image and copyright information in PMC

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