Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle

An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.

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Attitude heading reference algorithm based on transformed cubature Kalman filter

Measurement and Control ◽

10.1177/0020294020944941 ◽

2020 ◽

Vol 53 (7-8) ◽

pp. 1446-1453

Author(s):

Yong-jun Yu ◽

Xiang Zhang ◽

M Sadiq Ali Khan

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicle ◽

Reference System ◽

Orthogonal Transformation ◽

Estimation Algorithm ◽

Attitude Estimation ◽

Measurement Unit ◽

Sampling Point ◽

Cubature Kalman Filter ◽

Aerial Vehicle

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier’s motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s2, and the accuracy of attitude reaches 1°.

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A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter

Sensors ◽

10.3390/s20195459 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5459 ◽

Cited By ~ 1

Author(s):

Xuliang Lu ◽

Zhongbin Wang ◽

Chao Tan ◽

Haifeng Yan ◽

Lei Si ◽

...

Keyword(s):

Kalman Filter ◽

Measurement Error ◽

Unscented Kalman Filter ◽

Attitude Estimation ◽

Measurement Unit ◽

Hydraulic Support ◽

Sensing System ◽

Rock Structure ◽

Gradient Descent Algorithm ◽

Yaw Angle

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

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Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽

10.3390/s20030919 ◽

2020 ◽

Vol 20 (3) ◽

pp. 919 ◽

Cited By ~ 3

Author(s):

Hao Du ◽

Wei Wang ◽

Chaowen Xu ◽

Ran Xiao ◽

Changyin Sun

Keyword(s):

Data Fusion ◽

State Estimation ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Position Estimation ◽

Sensor Data ◽

Measurement Unit ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter

Advances in Space Research ◽

10.1016/j.asr.2018.10.003 ◽

2019 ◽

Vol 63 (2) ◽

pp. 1038-1050 ◽

Cited By ~ 19

Author(s):

R.V. Garcia ◽

P.C.P.M. Pardal ◽

H.K. Kuga ◽

M.C. Zanardi

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Nonlinear Filtering ◽

Unscented Kalman Filter ◽

Real Data ◽

Attitude Estimation ◽

Spacecraft Attitude ◽

Cubature Kalman Filter

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A Multi-sensor Data Fusion Algorithm Based on Unscented Kalman Filter for the Attitude Estimation of UAV

Journal of Physics Conference Series ◽

10.1088/1742-6596/1965/1/012001 ◽

2021 ◽

Vol 1965 (1) ◽

pp. 012001

Author(s):

Hongwei Wu ◽

Yinke Dou ◽

Jianlong Liu

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Unscented Kalman Filter ◽

Attitude Estimation ◽

Sensor Data ◽

Fusion Algorithm ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion

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Sensor fault–tolerant control of a quadrotor unmanned aerial vehicle

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/09544100211015587 ◽

2021 ◽

pp. 095441002110155

Author(s):

Mehmet Gokberk Patan ◽

Fikret Caliskan

Keyword(s):

Kalman Filter ◽

Control System ◽

Unmanned Aerial Vehicle ◽

Unscented Kalman Filter ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Sensor Faults ◽

Cubature Kalman Filter ◽

Non Linear ◽

Aerial Vehicle

This article handles the issue of fault-tolerant control of a quadrotor unmanned aerial vehicle (UAV) in the existence of sensor faults. A general non-linear model of the quadrotor is presented. Several non-linear Kalman filters namely, the extended Kalman filter, the unscented Kalman filter and the cubature Kalman filter (CKF) are utilized to estimate the states of the quadrotor and to compare the estimation performances. Some flight scenarios are simulated, and the simulation results show that the CKF has the smallest estimation error as expected in theory. Control of the quadrotor heavily depends on the measured values received from sensors. Therefore, the control system requires fault-free sensors. However, small quadrotors and UAVs are mostly equipped with low-cost and low-quality sensors, and hence, they may fail to indicate correct measurement values. If the sensors are faulty, then the control system itself should be actively tolerant to sensor faults. Measurements of these kinds of sensors suffer from bias and external noise due to temperature variations, vibration and other external conditions. Since the bias is one of the very common faults in these sensors, a sensor bias is taken into consideration as a fault and occurs abruptly at a certain time and continues throughout the considered scenarios. By using the residual signals generated by the non-linear filters, sensor faults are detected and isolated. Then, two different methods are proposed for removing the effects of faults and achieving active fault–tolerant control. The effectiveness of the presented two techniques is shown in the simulations.

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Performance Analysis of Alignment Process of MEMS IMU

International Journal of Navigation and Observation ◽

10.1155/2012/731530 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 10

Author(s):

Vadim Bistrov

Keyword(s):

Kalman Filter ◽

Data Processing ◽

Attitude Estimation ◽

Sensor Data ◽

Inertial System ◽

Measurement Unit ◽

Mems Sensor ◽

Alignment Process ◽

Mems Imu ◽

The Impact

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

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