Multi-sensor fusion techniques for state estimation of micro air vehicles

Daniel Donavanik; Alexander Hardt-Stremayr; Gregory Gremillion; Stephan Weiss; William Nothwang

doi:10.1117/12.2224162

25 May 2016 Multi-sensor fusion techniques for state estimation of micro air vehicles

Daniel Donavanik, Alexander Hardt-Stremayr, Gregory Gremillion, Stephan Weiss, William Nothwang

Author Affiliations +

Proceedings Volume 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII; 98361V (2016) https://doi.org/10.1117/12.2224162
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Aggressive flight of micro air vehicles (MAVs) in unstructured, GPS-denied environments poses unique challenges for estimation of vehicle pose and velocity due to the noise, delay, and drift in individual sensor measurements. Maneuvering flight at speeds in excess of 5 m/s poses additional challenges even for active range sensors; in the case of LIDAR, an assembled scan of the vehicles environment will in most cases be obsolete by the time it is processed. Multi-sensor fusion techniques which combine inertial measurements with passive vision techniques and/or LIDAR have achieved breakthroughs in the ability to maintain accurate state estimates without the use of external positioning sensors. In this paper, we survey algorithmic approaches to exploiting sensors with a wide range of nonlinear dynamics using filter and bundle-adjustment based approaches for state estimation and optimal control. From this foundation, we propose a biologically-inspired framework for incorporating the human operator in the loop as a privileged sensor in a combined human/autonomy paradigm.

Citation Download Citation

Daniel Donavanik, Alexander Hardt-Stremayr, Gregory Gremillion, Stephan Weiss, and William Nothwang "Multi-sensor fusion techniques for state estimation of micro air vehicles", Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 98361V (25 May 2016); https://doi.org/10.1117/12.2224162

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