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SLAM-based return to take-off point for UAS

 
: Bender, D.; Koch, W.; Cremers, D.

:

Lee, S. ; Institute of Electrical and Electronics Engineers -IEEE-:
Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System : An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), held in Daegu, Korea, 16-22 November 2017
Cham: Springer International Publishing, 2018 (Lecture notes in electrical engineering 501)
ISBN: 978-3-319-90508-2 (Print)
ISBN: 978-3-319-90509-9 (Online)
S.168-185
International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) <13, 2017, Daegu/Korea>
Englisch
Konferenzbeitrag
Fraunhofer FKIE ()

Abstract
Up to the present day, GPS signals are the key component in almost all outdoor navigation tasks of robotic platforms. To obtain the platform pose, comprising the position as well as the orientation, and receive information at a higher frequency, the GPS signals are commonly used in a GPS-corrected inertial navigation system (INS). However, the GPS is a critical single point of failure for unmanned aircraft systems (UAS). We propose an approach which creates a metric map of the overflown area by fusing camera images with inertial and GPS data during normal UAS operation and use this map to steer the system efficiently to its home position in the case of an GPS outage. A naive approach would follow the previously traveled path and get accurate pose estimates by comparing the current camera image with the previously created map. The presented procedure allows the usage of shortcuts through unexplored areas to minimize the travel distance. Thereby, we ensure to reach the st arting point by taking into consideration the maximal positional drift while performing pure visual navigation in unknown areas. We achieved close to optimal results in intensive numerical studies and demonstrate the usage of the algorithm in a realistic simulation environment and the real-world.

: http://publica.fraunhofer.de/dokumente/N-516020.html