Vision-Inertial-based Adaptive State Estimation of Hexacopter with a Cable-Suspended Load - Wang, Siqiang; Liu, Jianheng; Jiang, Xin; Chen, Haoyao (2022 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2022)
Tracking a given trajectory by aerial vehicles without an external positioning system is challenging in the natural environment since the computation power and sensory of the online positioning process are limited. We design a novel and theoretically-proved state estimator for an unmanned aerial vehicle with a cable-suspended load based on stability theory. The asymptotical convergence of our system is proved by the Lyapunov theory. The visual-inertial estimation algorithm, which we propose, consumes lower computation power compared with the optimization-based methods and provides high-frequency state estimation for the system. Several Gazebo-based simulations are performed to verify that the proposed algorithm is better than one state-of-the-art visual-inertial-based algorithm. The simulation results demonstrate the convergence and efficiency of the algorithm.