Modern UAVs, equipped with advanced AI, can detect objects, classify targets, and understand terrain. Yet, they still face a critical limitation: they don't know their exact location; they estimate it. This distinction is crucial for flight safety. State estimation remains vital for UAVs to understand their attitude, speed, and drift. While AI models excel at learning patterns, they struggle with the precision needed for flight-critical questions.

AI models answer questions like 'What is this object?' or 'Where should I go next?' However, flight-critical queries such as 'What is my attitude right now?' require precise state estimation. UAVs rely on sensors like IMUs, GPS, barometers, and magnetometers, which are prone to drift, lag, and disturbances. State estimation processes, such as Kalman filters, infer the most likely state of the system by filtering noise and delay.

The real world is noisy and delayed, making AI's role in perception and prediction complementary to state estimation. While AI can help UAVs understand the world, state estimation helps them understand themselves. This self-awareness is fundamental for safe and effective flight. UAV systems should maintain a healthy architecture where state estimation is physics-based and deterministic, control is fast and safety-critical, and AI aids in perception and prediction.

As UAV technology advances, the separation of responsibilities between AI and state estimation will continue to be vital. Future developments may see AI models improving state estimation, but the fundamental need for precise, explainable state information will remain. Engineers must ensure that UAVs can reliably estimate their state to navigate safely and effectively.