Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
arXiv:2606.19176v1 Announce Type: cross Abstract: Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight while emulating photorealistic maritime environments. Rendered maritime views are processed onboard by a deep transformer-based monocular pose estimator. Delayed vision measurements are fused with high-rate IMU data using a delayed Kalman filter to provide consistent state estimate
The increasing sophistication of AI and sensor fusion, combined with the escalating need for autonomous capabilities in challenging environments, drives the development of such validation frameworks.
This development significantly de-risks and accelerates the deployment of autonomous UAVs in maritime operations, which has critical defence and logistics implications.
The ability to validate complex autonomous systems in a hardware- and vision-in-the-loop environment drastically reduces the cost, time, and safety risks associated with real-world testing.
- · Autonomous system developers
- · Defence contractors
- · Maritime logistics
- · Sensor manufacturers
- · Traditional manual inspection services
- · High-cost at-sea testing facilities
More rapid deployment of autonomous maritime UAVs for surveillance, logistics, and combat support.
Increased pressure on adversaries to develop countermeasures against advanced autonomous naval assets.
Potential for a new arms race in autonomous naval warfare capabilities and cyber-physical security measures.
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Read at arXiv cs.AI