{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:26:30Z","timestamp":1760243190564,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2015,12,19]],"date-time":"2015-12-19T00:00:00Z","timestamp":1450483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s151229903","type":"journal-article","created":{"date-parts":[[2015,12,21]],"date-time":"2015-12-21T10:43:59Z","timestamp":1450694639000},"page":"32031-32044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators"],"prefix":"10.3390","volume":"15","author":[{"given":"Gabriele","family":"Ligorio","sequence":"first","affiliation":[{"name":"The BioRobotics Institute, Scuola Superiore Sant\u2019Anna, Piazza Martiri della Libert\u00e0 33, Pisa 56125, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-6498","authenticated-orcid":false,"given":"Angelo","family":"Sabatini","sequence":"additional","affiliation":[{"name":"The BioRobotics Institute, Scuola Superiore Sant\u2019Anna, Piazza Martiri della Libert\u00e0 33, Pisa 56125, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1177\/0278364913509675","article-title":"Camera-IMU-based localization: Observability analysis and consistency improvement","volume":"33","author":"Hesch","year":"2014","journal-title":"Int. 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