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基于 MARG 的算法有效开发的开源测试环境。

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

机构信息

Department of Control Engineering and Information Technology, University of Dunaújváros, Táncsics Mihály u. 1, 2400 Dunaújváros, Hungary.

出版信息

Sensors (Basel). 2021 Feb 8;21(4):1183. doi: 10.3390/s21041183.

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.

摘要

本文提出了一个用于开发、调整和性能评估基于磁、角速度和重力(MARG)的滤波器(如姿态估计器和分类算法)的开源环境。该环境同时可在 ROS/Gazebo 和 MATLAB/Simulink 中使用,并且包含一个六自由度(6 DOF)测试平台,可在三维(3D)空间中同时移动和旋转 MARG 单元。由于 MARG 基估计的质量在动态情况下变得至关重要,因此所提出的测试平台旨在模拟不同的加速和振动情况,以及现实的磁干扰事件。此外,在这些模拟系统行为期间,同时支持真实姿态状态(真实值)和原始传感器数据的同时采集。因此,测试环境执行期望的静态和动态系统条件的混合,并且提供的数据库促进了传感器融合算法的有效分析。本文系统地描述了所提出的测试平台的结构,从机械特性、数学建模和关节控制器综合到实现结果。此外,还提出了一个流行的姿态估计算法的调整案例研究,以突出开发的开源环境的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d48f/7919258/ed454ec4445b/sensors-21-01183-g001.jpg

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