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A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.

Gabriele Ligorio¹, Angelo Maria Sabatini²

¹The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa 56125, Italy. g.ligorio@sssup.it.

Sensors (Basel, Switzerland)

|December 26, 2015

Summary

This summary is machine-generated.

This study introduces a simulation framework for evaluating sensor fusion pose estimators using magnetic/inertial measurement units (MIMUs) and cameras. The framework was validated with real-world data, showing good performance for sensor fusion algorithm benchmarking.

Keywords:

performance evaluation sensor fusion sensor modeling simulation

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Area of Science:

Robotics
Sensor Fusion
Computer Vision

Background:

Accurate performance evaluation of sensor fusion estimators is crucial, especially with real-world data.
Simulation is a key tool for benchmarking sensor fusion algorithms.

Purpose of the Study:

To present a simulation framework for assessing sensor fusion-based pose estimator performance.
To validate the simulation environment using real-world motion tracking data.

Main Methods:

Developed a simulation framework incorporating magnetic/inertial measurement units (MIMUs) and cameras.
Included typical nuisance factors for each sensor.
Validated the simulation using real-life sensor data.

Main Results:

The simulation framework demonstrated a high degree of accuracy, with a maximum mismatch of approximately 5% for camera simulation.
A high correlation (0.90) was observed for a gyroscope axis, indicating reliable simulation.
Presented a benchmarking example using an extended Kalman filter for pose estimation.

Conclusions:

The proposed simulation framework is effective for evaluating sensor fusion-based pose estimators.
The validated framework provides a reliable tool for algorithm development and benchmarking.
The framework supports the integration of multiple sensor modalities for comprehensive analysis.