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Map-based drone homing using shortcuts

 
: Bender, Daniel; Koch, Wolfgang; Cremers, Daniel

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 : November 16-18, 2017, Daegu, Korea
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-6064-1
ISBN: 978-1-5090-6063-4
ISBN: 978-1-5090-6065-8
S.505-511
International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) <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). The GPS is a critical single point of failure, especially for autonomous drones. We propose an approach which creates a metric map of the observed area by fusing camera images with inertial and GPS data during its normal operation and use this map to steer a drone 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 starting 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 we demonstrate the usability of the algorithm in a realistic simulation environment.

: http://publica.fraunhofer.de/dokumente/N-481711.html