A Complementary Filter for High-fidelity Attitude Estimation based on Decoupled Linear/Nonlinear Properties of Inertial Sensors

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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