arXiv:2605.24449v1 Announce Type: cross Abstract: Although quadcopters boast impressive traversal capabilities enabled by their omnidirectional maneuverability, the need for continuous pilot control in complex environments impedes their application in GNSS and telemetry-denied scenarios. To this end, we propose a novel sensorimotor policy that uses stereo-vision depth and visual-inertial odometry (VIO) to autonomously navigate through obstacles in an unknown environment to reach a goal point. The policy is comprised of a pre-trained autoencoder as the perception head followed by a planning and
Advances in reinforcement learning and computer vision are converging to enable more robust autonomous navigation in complex, GPS-denied environments.
This research provides a critical step towards fully autonomous aerial systems, capable of operating independently in situations where human control or external aid is impossible, impacting defense, logistics, and exploration.
The ability of quadcopters to navigate truly autonomously in unknown, obstructed, and signal-denied environments improves substantially, reducing the need for continuous human pilot control.
- · Defence sector
- · Logistics and delivery companies
- · Robotics hardware manufacturers
- · AI/ML research labs
- · Manual drone piloting services
- · Systems heavily reliant on GNSS for navigation
Increased deployment of autonomous drones for critical missions in challenging environments.
Accelerated development of robust, real-time sensing and AI decision-making for various robotic platforms.
Reduced human risk exposure in dangerous operational zones, leading to shifts in mission planning and resource allocation for military and emergency services.
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