Performance Evaluation of Direction Cosine Matrix and Madgwick’s as 3D Orientation Estimation Algorithm for Estimating Ground Contour

Mapping Intimacies ◽

10.21203/rs.3.rs-34412/v1 ◽

2020 ◽

Author(s):

Eri Prasetyo Wibowo ◽

Imam Purwanto ◽

Dennis Aprilla Christie ◽

Purnawarman Musa ◽

Busono Soerowirdjo

Keyword(s):

Interval Estimation ◽

Direction Cosine ◽

Dead Reckoning ◽

Estimation Algorithm ◽

Estimation Problem ◽

Mobile Systems ◽

Orientation Estimation ◽

Trajectory Estimation ◽

Contour Interval ◽

Direction Cosine Matrix

Abstract Contour interval is elevation difference between two contour lines that are adjacent and parallel. Ground Contour interval is an important information that must be provided in a construction. Ground contour interval estimation problem could be viewed as a 3D trajectory estimation problem. One framework, called dead reckoning, is often used for motion (position and orientation) tracking of a mobile systems over time. Dead reckoning requires a precise 3D orientation estimation algorithm. Two competing algorithms are Direction Cosine Matrix and Madgwick’s. To ensure the 3D trajectory estimation gives an accurate result, these two algorithms must be carefully and thoroughly evaluated. This research is part of a complete research on the development of a ground contour estimation and only focuses on comparison between two 3D orientation estimation algorithm.

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Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Journal of Sensors ◽

10.1155/2017/8542153 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Siwen Guo ◽

Jin Wu ◽

Zuocai Wang ◽

Jide Qian

Keyword(s):

Kalman Filter ◽

Process Model ◽

Attitude Determination ◽

Angular Rate ◽

Estimation Algorithm ◽

Measurement Model ◽

Estimation Accuracy ◽

Orientation Estimation ◽

Kinematic Equation ◽

Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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Computational Errors in the Generation of the Direction Cosine Matrix in a Strapdown System

Navigation ◽

10.1002/j.2161-4296.1967.tb02200.x ◽

1967 ◽

Vol 14 (1) ◽

pp. 95-104 ◽

Cited By ~ 2

Author(s):

AVERY A. MORGAN

Keyword(s):

Direction Cosine ◽

Computational Errors ◽

Direction Cosine Matrix

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A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

International Journal of Navigation and Observation ◽

10.1155/2015/503814 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 11

Author(s):

Heikki Hyyti ◽

Arto Visala

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Low Cost ◽

Direction Cosine ◽

Estimation Algorithm ◽

Calibration Method ◽

Attitude Estimation ◽

Mems Sensors ◽

Acceleration Sensors ◽

Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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Attitude reconstruction from strap-down rate gyros using power series

Journal of Navigation ◽

10.1017/s0373463321000023 ◽

2021 ◽

pp. 1-19

Author(s):

Habib Ghanbarpourasl

Keyword(s):

Power Series ◽

Taylor Series ◽

Direction Cosine ◽

Analytical Description ◽

Double Precision ◽

Angular Velocity Vector ◽

Higher Order Terms ◽

Direction Cosine Matrix ◽

The Stability ◽

Better Than

Abstract This paper introduces a power series based method for attitude reconstruction from triad orthogonal strap-down gyros. The method is implemented and validated using quaternions and direction cosine matrix in single and double precision implementation forms. It is supposed that data from gyros are sampled with high frequency and a fitted polynomial is used for an analytical description of the angular velocity vector. The method is compared with the well-known Taylor series approach, and the stability of the coefficients’ norm in higher-order terms for both methods is analysed. It is shown that the norm of quaternions’ derivatives in the Taylor series is bigger than the equivalent terms coefficients in the power series. In the proposed method, more terms can be used in the power series before the saturation of the coefficients and the error of the proposed method is less than that for other methods. The numerical results show that the application of the proposed method with quaternions performs better than other methods. The method is robust with respect to the noise of the sensors and has a low computational load compared with other methods.

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Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211043666 ◽

2021 ◽

pp. 014233122110436

Author(s):

Hao Yang ◽

Yilian Zhang ◽

Wei Gu ◽

Fuwen Yang ◽

Zhiquan Liu

Keyword(s):

State Estimation ◽

Estimation Algorithm ◽

The State ◽

Estimation Problem ◽

Filtering Method ◽

Automatic Guided Vehicles ◽

Tracking Process ◽

Set Membership ◽

Text Filtering ◽

System States

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

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Multi-sensor attitude algorithm design for a low-cost strap-down system based on the direction cosine matrix

Civil, Architecture and Environmental Engineering ◽

10.1201/9781315226187-275 ◽

2017 ◽

pp. 1491-1498

Keyword(s):

Low Cost ◽

Algorithm Design ◽

Direction Cosine ◽

Direction Cosine Matrix

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Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

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

10.1177/0954410017713771 ◽

2017 ◽

Vol 232 (12) ◽

pp. 2364-2377 ◽

Cited By ~ 2

Author(s):

Xiaogang Wang ◽

Wutao Qin ◽

Naigang Cui ◽

Yu Wang

Keyword(s):

State Estimation ◽

Estimation Algorithm ◽

Ballistic Missile ◽

Measurement Noise ◽

Estimation Accuracy ◽

Trajectory Estimation ◽

Information Filter ◽

Non Gaussian ◽

Time Update ◽

High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

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