Filter-Based Online System-Parameter Estimation for Multicopter UAVs

Accurate system modeling and identification gain importance as tasks executed by autonomously acting unmanned aerial vehicles (UAVs) get more complex and demanding. This paper presents a Bayesian filter approach to online and continuously identify the system parameters; sensor suite calibration states; and vehicle navigation states in a holistic framework. Previous work only tackles subsets of the overall state vector during dedicated phases (e.g.; motionless; online during flight; post-processing). These works often introduce the artificial so-called body frame forcing assumptions on system states; such as the inertia matrix’s principal axes orientation. Our approach estimates the entire state vector in the (usually not precisely known) center of mass; eliminating several assumptions caused by the artificially introduced body frame in other work. Since our approach also estimates geometric states such as the rotor and sensor placements; no hand-made measures to the unknown center of mass are required – the system is fully self-calibrating. A detailed discussion on the system’s observability reveals additionally required (different) measurements for a theoretical and a real N-arm multicopter. We show that easy and precise hand-measurable quantities in real applications can provide the required information. Statistically relevant simulations in Gazebo/RotorS providing ground truth for all states yet having realistic physics validate all our findings.