ArticleSensors (Basel, Switzerland)2021
An Open-Source Test Environment for Effective Development of MARG-Based Algorithms.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A Novel Shallow Neural Network-Augmented Pose Estimator Based on Magneto-Inertial Sensors for Reference-Denied Environments.Sensors (Basel, Switzerland) · 2025Article
- Research on Gradient-Descent Extended Kalman Attitude Estimation Method for Low-Cost MARG.Micromachines · 2022Article
- Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points.Sensors (Basel, Switzerland) · 2021Article
- Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms.Sensors (Basel, Switzerland) · 2021Article
- An Open-Source Test Environment for Effective Development of MARG-Based Algorithms.Sensors (Basel, Switzerland) · 2021Article
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1 author.
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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.
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