scholarly journals A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

Sensors ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 15138-15158 ◽  
Author(s):  
José Guerrero-Castellanos ◽  
Heberto Madrigal-Sastre ◽  
Sylvain Durand ◽  
Lizeth Torres ◽  
German Muñoz-Hernández
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Nonlinear Observer ◽  
Robust Nonlinear Observer

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.

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.

Nonlinear observer for real-time attitude estimation

2009 ◽  
Author(s):  
W. F. Guerrero-Sanchez ◽  
J. F. Guerrero-Castellanos ◽  
R. Juarez-Salazar ◽  
B. B. Salmeron-Quiroz
Keyword(s):  
Real Time ◽  
Attitude Estimation ◽  
Nonlinear Observer

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.

scholarly journals Design of an Attitude and Heading Reference System Based on Distributed Filtering for Small UAV

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Long Zhao ◽  
Qing Yun Wang
Keyword(s):  
Real Time ◽  
Reference System ◽  
Low Cost ◽  
Attitude Estimation ◽  
The Real ◽  
Small Uav ◽  
Development Procedure ◽  
Long Time ◽  
Initial Attitude ◽  
Distributed Filter

A development procedure for a low-cost attitude and heading reference system (AHRS) based on the distributed filter has been proposed. The AHRS consists of three single-axis accelerometers, three single-axis gyroscopes, and one 3-axis digital compass. The initial attitude estimation is readily accomplished by using the complementary filtering. The attitude estimation for UAV flying in the real time is realized by using the three low orders EKF. The validation results show that the estimated orientations of the developed AHRS are within the acceptable region, and AHRS can give a stabilized attitude and heading information for a long time.

Calibration of Low Cost IMU’s Inertial Sensors for Improved Attitude Estimation

2020 ◽  
Vol 100 (3-4) ◽  
pp. 1015-1029
Author(s):  
Mingjie Dong ◽  
Guodong Yao ◽  
Jianfeng Li ◽  
Leiyu Zhang
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation

scholarly journals Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4055 ◽  
Author(s):  
Farzan Farhangian ◽  
Rene Landry
Keyword(s):  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Pi Controller ◽  
Attitude Estimation ◽  
Error Prediction ◽  
Accuracy Improvement ◽  
Body Tracking ◽  
Human Body Tracking ◽  
Prediction Filter

Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope’s error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

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