Model-Based Attitude Estimation for Multicopters

Baranek, Radek; Šolc, František

doi:10.15598/aeee.v12i5.1151

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The traditional drag model only contains the velocity related item. Its accuracy is affected by maneuver flight [27]. In this paper, some attitude motion related items are considered to improve the model accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

Lyu

Wang

Lai

et al. 2019

Sensors

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In this paper, a drag model-aided fault-tolerant state estimation method is presented for quadrotors. Firstly, the drag model accuracy was improved by modeling an angular rate related item and an angular acceleration related item, which are related with flight maneuver. Then the drag model, light detection and ranging (LIDAR), and inertial measurement unit (IMU) were fused based on the Federal Kalman filter frame. In the filter, the LIDAR estimation fault was detected and isolated, and the disturbance to the drag model was estimated and compensated. Some experiments were carried out, showing that the velocity and position estimation were improved compared with the traditional LIDAR/IMU fusion scheme.

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Section: Introductionmentioning

confidence: 99%

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

Lyu

Wang

Lai

et al. 2019

Sensors

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“…However, there is a no wind assumption for the current MA-IEs (Hanley, 2015). Therefore, although several MA-IEs have been proved to work well under no wind environments, the velocity accuracy will decrease in a windy environment (Baranek and Solc, 2014). Most wind velocity estimation methods are based on the airspeed sensor (Langelaan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Model-aided Optical Flow/Inertial Sensor Fusion Method for a Quadrotor

et al. 2016

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In this paper, a fault-tolerant velocity estimation method is proposed for quadrotors in a GPS denied environment. A novel filter is developed in light of the quadrotor model and measurements from optical flow and inertial sensors. The proposed filter is capable of detecting and isolating the optical flow sensor faults, by which the velocity estimation accuracy and stability will be improved. It is also demonstrated that the wind velocity is observable in the proposed filter. Therefore, the new filter can also be implemented in a windy environment, which is a significant improvement to the previous model-aided inertial sensor estimator. At the end, some simulations are carried out to verify the advantages of our method.

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“…20 Researchers have studied how wind and errors in model parameters impact the attitude estimate of an IMU and magnetometer navigation system that also uses the H-force drag in its dynamic model. 23 In this paper, we present a new observer using only measurements from a low cost IMU on-board a quadrotor. We use the H-force drag model developed in previous work, however, we also include the Coriolis effect in our model.…”

Section: Introductionmentioning

confidence: 99%

An Improved Model-Based Observer for Inertial Navigation for Quadrotors with Low Cost IMUs

Hanley

2016

AIAA Guidance, Navigation, and Control Conference

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In this paper, we present a model-based observer for inertial navigation of quadrotors and other multirotor aircraft. We include in our model a Coriolis term that has been neglected in prior work. Doing so allows us to estimate the entire velocity vector in the quadrotor's frame of reference-including along the z-axis of this frame-with data only from a low-cost inertial measurement unit (IMU): something that has not been demonstrated previously. An observability analysis predicts that our proposed observer will perform well. Experimental results over 110 flight trials verify this prediction, showing that our proposed observer achieves lower root mean square error than three other stateof-the-art model-based observers.

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Model-Based Attitude Estimation for Multicopters

Cited by 4 publications

References 9 publications

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

A Model-aided Optical Flow/Inertial Sensor Fusion Method for a Quadrotor

An Improved Model-Based Observer for Inertial Navigation for Quadrotors with Low Cost IMUs

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