Least square estimation-based adaptive complimentary filter for attitude estimation

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.

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

Sensors ◽

10.3390/s20236752 ◽

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.

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Attitude Estimation with Complementary Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3781 ◽

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.

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

IEEE Sensors Journal ◽

10.1109/jsen.2016.2589660 ◽

2016 ◽

Vol 16 (18) ◽

pp. 6997-7007 ◽

Cited By ~ 98

Author(s):

Jin Wu ◽

Zebo Zhou ◽

Jingjun Chen ◽

Hassen Fourati ◽

Rui Li

Keyword(s):

Low Cost ◽

Attitude Estimation ◽

Complementary Filter

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

Sensors ◽

10.3390/s131115138 ◽

2013 ◽

Vol 13 (11) ◽

pp. 15138-15158 ◽

Cited By ~ 19

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

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.663.254 ◽

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.

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