Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization
2013 (English)In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2013, 3993-3999 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2013. 3993-3999 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Accurate estimation, Computational requirements, Heterogeneous sensors, Pareto-optimization, Robotic applications, Sensor fusion algorithms, Statistical characteristics, Statistical properties
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-143165DOI: 10.1109/IROS.2013.6696927ScopusID: 2-s2.0-84893752076ISBN: 978-146736358-7OAI: oai:DiVA.org:kth-143165DiVA: diva2:705826
2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013; Tokyo; Japan; 3 November 2013 through 8 November 2013
QC 201403182014-03-182014-03-172014-03-18Bibliographically approved