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Precise system identification is an important aspect of adequate control design and parameter definition to allow for accurate and reliable navigation. While this is well known in robotics, the community working with small rotorcraft Unmanned Aerial Vehicles (UAVs) has yet to discover the benefits. In contrast to existing work, which often performs offline or deterministic (i.e. closed-form) system identification, we present a probabilistic approach to the online estimation of system identification parameters and self-calibration states. Instead of decoupling system identification and state estimation for vehicle control, we merge the entire process into a holistic probabilistic framework to allow self-awareness and self-healing. Our observability analysis shows that most of the system identification parameters are observable and converge quickly to the optimal value using a combination of inertial cues, dynamic modeling, and an additional exteroceptive sensor. We support our theoretical findings with extensive tests simulating realistic data in Gazebo. |
