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Sensors 2015, 15(12), 32031-32044;

A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators

The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 17 November 2015 / Revised: 14 December 2015 / Accepted: 17 December 2015 / Published: 19 December 2015
(This article belongs to the Section Physical Sensors)
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In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented. View Full-Text
Keywords: simulation; sensor modeling; sensor fusion; performance evaluation simulation; sensor modeling; sensor fusion; performance evaluation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ligorio, G.; Sabatini, A.M. A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators. Sensors 2015, 15, 32031-32044.

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