Literature DB >> 33567563

An Open-Source Test Environment for Effective Development of MARG-Based Algorithms.

Ákos Odry1.   

Abstract

This paper presents an open-source environment for development, tuning, and performance evaluation of magnetic, angular rate, and gravity-based (MARG-based) filters, such as pose estimators and classification algorithms. The environment is available in both ROS/Gazebo and MATLAB/Simulink, and it contains a six-degrees of freedom (6 DOF) test bench, which simultaneously moves and rotates an MARG unit in the three-dimensional (3D) space. As the quality of MARG-based estimation becomes crucial in dynamic situations, the proposed test platform intends to simulate different accelerating and vibrating circumstances, along with realistic magnetic perturbation events. Moreover, the simultaneous acquisition of both the real pose states (ground truth) and raw sensor data is supported during these simulated system behaviors. As a result, the test environment executes the desired mixture of static and dynamic system conditions, and the provided database fosters the effective analysis of sensor fusion algorithms. The paper systematically describes the structure of the proposed test platform, from mechanical properties, over mathematical modeling and joint controller synthesis, to implementation results. Additionally, a case study is presented of the tuning of popular attitude estimation algorithms to highlight the advantages of the developed open-source environment.

Entities:  

Keywords:  Kalman filter; MARG; attitude estimation; complementary filter; inertial measurement unit; sensor fusion; test environment

Year:  2021        PMID: 33567563      PMCID: PMC7919258          DOI: 10.3390/s21041183

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  7 in total

1.  An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking.

Authors:  Claudia Mazzà; Marco Donati; John McCamley; Pietro Picerno; Aurelio Cappozzo
Journal:  Gait Posture       Date:  2011-11-01       Impact factor: 2.840

2.  Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions.

Authors:  Jung Keun Lee; Edward J Park; Stephen N Robinovitch
Journal:  IEEE Trans Instrum Meas       Date:  2012-01-08       Impact factor: 4.016

3.  A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation.

Authors:  Xudong Wen; Chunwu Liu; Zhiping Huang; Shaojing Su; Xiaojun Guo; Zhen Zuo; Hao Qu
Journal:  Sensors (Basel)       Date:  2019-03-18       Impact factor: 3.576

4.  A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots.

Authors:  Ákos Odry; Istvan Kecskes; Peter Sarcevic; Zoltan Vizvari; Attila Toth; Péter Odry
Journal:  Sensors (Basel)       Date:  2020-02-01       Impact factor: 3.576

5.  Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs.

Authors:  Roberto G Valenti; Ivan Dryanovski; Jizhong Xiao
Journal:  Sensors (Basel)       Date:  2015-08-06       Impact factor: 3.576

6.  Performance Evaluation of Smartphone Inertial Sensors Measurement for Range of Motion.

Authors:  Quentin Mourcou; Anthony Fleury; Céline Franco; Frédéric Klopcic; Nicolas Vuillerme
Journal:  Sensors (Basel)       Date:  2015-09-15       Impact factor: 3.576

  7 in total
  1 in total

1.  Research on Gradient-Descent Extended Kalman Attitude Estimation Method for Low-Cost MARG.

Authors:  Ning Liu; Wenhao Qi; Zhong Su; Qunzhuo Feng; Chaojie Yuan
Journal:  Micromachines (Basel)       Date:  2022-08-09       Impact factor: 3.523

  1 in total

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