Name
Abstract | ||
|---|---|---|
In this work, we present a model-based estimation scheme for multi-rotor Micro Aerial Vehicles (MAVs). Although modeling approaches for MAVs have been presented in the past, these models have rarely been used for real-time state estimation onboard MAVs. Building on this work, we identify the most dominant effects and propose an easy-to-use calibration scheme for estimation of the model parameters. Given the calibration estimates for these parameters, we derive a state estimator where the state prediction of the indirect Extended Kalman Filter (EKF) is driven by a MAV model. Solely using measurements from the Inertial Measurement Unit (IMU) and a barometric pressure sensor - both available on almost every MAV - our model-based formulation keeps the estimated velocity of the MAV bounded in all directions, as opposed to state of the art IMU-model driven state estimators onboard MAVs. This is crucial for keeping MAVs airborne safely, for instance in the case of failures or re-initialization of vision based localization systems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICRA.2015.7139935 | IEEE International Conference on Robotics and Automation |
Keywords | Field | DocType |
Kalman filters,calibration,nonlinear filters,pressure sensors,space vehicles,state estimation,units (measurement),vehicle dynamics,EKF,IMU,barometric pressure sensor,calibration scheme,indirect extended Kalman filter,inertial measurement unit,model-based estimation scheme,multirotor micro aerial vehicles,real-time state estimation onboard MAV,system dynamics,vision based localization systems | Extended Kalman filter,Control theory,Accelerometer,Control engineering,Inertial measurement unit,System dynamics,Acceleration,Engineering,Calibration,Estimator,Aerodynamics | Conference |
Volume | Issue | ISSN |
2015 | 1 | 1050-4729 |
Citations | PageRank | References |
4 | 0.66 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Burri | 1 | 343 | 18.62 |
| Manuel Datwiler | 2 | 4 | 0.66 |
| Markus Achtelik | 3 | 710 | 32.78 |
| Roland Siegwart | 4 | 7640 | 551.49 |