Journal
SENSORS
Volume 21, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/s21041183
Keywords
MARG; attitude estimation; complementary filter; inertial measurement unit; Kalman filter; sensor fusion; test environment
Funding
- European Union [EFOP-3.6.1-16-2016-00003]
- [2020-4.1.1-TKP2020]
Ask authors/readers for more resources
This paper presents an open-source environment for developing, tuning, and evaluating MARG-based filters, including a 6 DOF test bench for simulating various scenarios and supporting real data acquisition. The database provided fosters effective analysis of sensor fusion algorithms, highlighting practical advantages.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available