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VeloBins achieves unprecedented accuracy in aerial inertial odometry by transforming velocity estimation into a classification task, eliminating the pitfalls of traditional regression methods.
Triangulation validity improves by 10.6 percentage points when accounting for communication delays, showcasing the importance of Age-of-Information in multi-agent systems.
Autonomous drones can now navigate high-speed races more reliably thanks to a visual-inertial system that ditches traditional PnP solvers for direct pixel reprojection, handling noisy data with as few as two visible features.