Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method...

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Veröffentlicht in:2013 IEEE/RSJ International Conference on Intelligent Robots and Systems S. 3993 - 3999
Hauptverfasser: Loianno, Giuseppe, Lippiello, Vincenzo, Fischione, Carlo, Siciliano, Bruno
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2013
Schriftenreihe:IEEE International Conference on Intelligent Robots and Systems
Schlagworte:
Accurate estimation
Computational requirements
Displacement measurement
Estimation
Heterogeneous sensors
Pareto optimization
Position measurement
Robot sensing systems
Robotic applications
Sensor fusion algorithms
Statistical characteristics
Statistical properties
Vehicles
Visualization
ISBN:1467363588, 9781467363587
ISSN:2153-0858
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Beschreibung
Zusammenfassung:Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical modelbased techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.
ISBN:1467363588
9781467363587
ISSN:2153-0858
DOI:10.1109/IROS.2013.6696927