A nonlinear complementary filter approach for MAV 3D-attitude estimation with low-cost MARG/ADS

2016 ◽  
Author(s):  
Lingling Wang ◽  
Li Fu ◽  
Xiaoguang Hu
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Complementary Filter ◽  
Filter Approach

Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

2016 ◽  
Vol 16 (18) ◽  
pp. 6997-7007 ◽  
Author(s):  
Jin Wu ◽  
Zebo Zhou ◽  
Jingjun Chen ◽  
Hassen Fourati ◽  
Rui Li
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Complementary Filter

Least square estimation-based adaptive complimentary filter for attitude estimation

2018 ◽  
Vol 41 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Parag Narkhede ◽  
Alex Noel Joseph Raj ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
Shashi Poddar
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Unmanned Vehicles ◽  
Least Square ◽  
Least Square Estimation ◽  
Micro Electro Mechanical Systems ◽  
Moving Vehicle ◽  
Complementary Filter ◽  
Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

scholarly journals Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6752
Author(s):  
Lingxiao Zheng ◽  
Xingqun Zhan ◽  
Xin Zhang
Keyword(s):  
Inertial Sensors ◽  
Filter Design ◽  
Low Cost ◽  
Measurement Model ◽  
Attitude Estimation ◽  
System Stability ◽  
Feature Points ◽  
Complementary Filter ◽  
Geometry Measurement ◽  
Implicit Geometry

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

2014 ◽  
Vol 663 ◽  
pp. 254-258
Author(s):  
Fargham Sandhu ◽  
Hazlina Selamat ◽  
Yahaya Md Sam
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Least Square ◽  
Low Grade ◽  
Ground Vehicles ◽  
Recursive Least Square ◽  
Stability And Control ◽  
Filter Approach ◽  
Navigational System ◽  
And Control

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

Attitude Estimation with Complementary Filter

2010 ◽  
Vol 44-47 ◽  
pp. 3781-3784
Author(s):  
Rui Hua Chang ◽  
Xiao Dong Mu ◽  
Xiao Wei Shen
Keyword(s):  
Solid State ◽  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Complementary Filter

An attitude estimation method is presented for a robot using low-cost solid-state inertial sensors. The attitude estimates are obtained from a complementary filter by combining the measurements from the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The results show that the estimation error is less than 1 degree compare to the reference attitude. It is a simple, yet effective method for attitude estimation, suitable for real-time implementation on a robot.

scholarly journals A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3066
Author(s):  
Egidio D’Amato ◽  
Vito Antonio Nardi ◽  
Immacolata Notaro ◽  
Valerio Scordamaglia
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Flight Control ◽  
Particle Filtering ◽  
Low Cost ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Raspberry Pi ◽  
Filter Approach

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

Embedded Attitude Estimation System for Quad-Rotor UAVs

2013 ◽  
Vol 321-324 ◽  
pp. 528-531
Author(s):  
Jing Ran Wu ◽  
Zhen Guo Sun ◽  
Qi Dong Ma ◽  
Wen Zeng Zhang
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Autonomous Flight ◽  
The Real ◽  
Ground Testing ◽  
Complementary Filter ◽  
Estimation System

An embedded attitude estimation system is developed for the autonomous flight of Quad-Rotor UAVs. The system hardware is composed of a DSP processor and low-cost MEMS sensors including a 3-axis gyroscope and a 3-axis accelerometer. A Complementary Filter fused the advantages of the gyroscope and accelerometer is designed and embedded on the DSP processor to estimate the real-time attitude. Ground testing experiments show that the system could meet the accuracy and robustness requirements for the Quad-Rotor UAVs attitude estimation.

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