title: A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.
abstract: 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.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy. g.ligorio@sssup.it.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Ligorio
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy. angelo.sabatini@sssup.it.
authors.forename: Angelo Maria
authors.initials: AM
authors.lastname: Sabatini
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D009038:Motion
keywords: performance evaluation; sensor fusion; sensor modeling; simulation