Enforcing Vision-Based Localization using Perception Constrained N-MPC for Multi-Rotor Aerial Vehicles

This work introduces a Nonlinear Model Predictive Control (N-MPC) for camera-equipped Unmanned Aerial Vehicles (UAVs), which controls at the motor level the UAV motion to ensure the quality of vision-based state estimation while performing other tasks. The controller ensures visibility over a sufficient amount of features, while optimizing their coverage, based on an assessment of the estimation quality. The controller works for the very broad class of generic multirotor UAVs, including platforms with any number of propellers, which can be both collinear, as in the quadrotor, and fixedlytilted. The low-level inputs are computed in real-time and realistically constrained, in terms of maximum motor torque. This allows the platform to exploit its full actuation capabilities to maintain the visibility over the set of points of interest. Our implementation is tested in Gazebo simulations and in mocap-free real experiments, and features a visual-inertial state estimation based on Kalman filter. The software is provided open-source.

Automated summary

This work introduces a Nonlinear Model Predictive Control (N-MPC) for camera-equipped Unmanned Aerial Vehicles (UAVs), which controls the UAV motion to ensure the quality of vision-based state estimation while performing other tasks. The controller is suitable for multirotor UAVs and provides open-source software.