Abstract
Precise real-time information about the position and orientation of robotic platforms as well as locally consistent point-clouds are essential for control, navigation, and obstacle avoidance. For years, GPS has been the central source of navigational information in airborne applications, yet as we aim for robotic operations close to the terrain and urban environments, alternatives to GPS need to be found. Fusing data from cameras and inertial measurement units in a nonlinear recursive estimator has shown to allow precise estimation of 6-Degree-of-Freedom (DoF) motion without relying on GPS signals. While related methods have shown to work in lab conditions since several years, only recently real-world robotic applications using visual-inertial state estimation found wider adoption. Due to the computational constraints, and the required robustness and reliability, it remains a challenge to employ a visual-inertial navigation system in the field. This paper presents our tightly integrated system involving hardware and software efforts to provide an accurate visual-inertial navigation system for low-altitude fixed-wing unmanned aerial vehicles (UAVs) without relying on GPS or visual beacons. In particular, we present a sliding window based visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm which provides real-time 6-DoF estimates for control. We demonstrate the performance on a small unmanned aerial vehicle and compare the estimated trajectory to a GPS based reference solution.
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Notes
- 1.
I.e. every value node is always connected to one or multiple factor nodes and vice versa [15].
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Acknowledgements
The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n°600958 (SHERPA) and was sponsored by Aurora Flight Sciences.
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Hinzmann, T. et al. (2016). Monocular Visual-Inertial SLAM for Fixed-Wing UAVs Using Sliding Window Based Nonlinear Optimization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_51
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