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 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 demonstrate the usage of the algorithm in a realistic simulation environment and the real-world.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bender, D., Cremers, D., Koch, W.: A position free boresight calibration for INS-camera systems. In: 2016 International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 52–57 (2016)
Bender, D., Cremers, D., Koch, W.: Map-based drone homing using shortcuts. In: 2017 International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 505–511 (2017)
Bender, D., Rouatbi, F., Schikora, M., Cremers, D., Koch, W.: Scaling the world of monocular SLAM with INS-measurements for UAS navigation. In: 2016 19th International Conference on Information Fusion (FUSION), pp. 1493–1500 (2016)
Chapuis, N.: Les opérations structurantes dans la connaissance de l’espace chez les mammifères: détour, raccourci et retour. Ph.D. thesis, Université Aix-Marseille 2 (1988)
Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1449–1456 (2013)
Giusti, A., Guzzi, J., Cireşan, D.C., He, F.L., Rodriguez, J.P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Di Caro, G., Scaramuzza, D., Gambardella, L.M.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1(2), 661–667 (2016)
Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2149–2154 (2004)
Liu, H., Jiang, R., Hu, W., Wang, S.: Navigational drift analysis for visual odometry. Comput. Inform. 33(3), 685–706 (2014)
Meyer, J.A., Filliat, D.: Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies. Cogn. Syst. Res. 4(4), 283–317 (2003)
Meyer, J., Sendobry, A., Kohlbrecher, S., Klingauf, U., von Stryk, O.: Comprehensive simulation of quadrotor UAVs using ROS and Gazebo. In: Simulation, Modeling, and Programming for Autonomous Robots, pp. 400–411. Springer, Heidelberg (2012)
Nelson, R.C.: Visual homing using an associative memory. Bio. Cybern. 65(4), 281–291 (1991)
Nieves, H.: The City: 3D Model. http://sharecg.com/v/79711/gallery/5/3D-Model/The-City (2015). Accessed 20 Feb 2018
Pomerleau, D.A.: Neural network based autonomous navigation. In: Vision and Navigation, pp. 83–93. Springer, Boston (1990)
SBG Systems: Ellipse Series: Miniature High Performance Inertial Sensors: technical data sheet. https://www.sbg-systems.com/docs/Ellipse_Series_Leaflet.pdf (2015). Accessed 3 Oct 2017
Sibley, G., Mei, C., Reid, I., Newman, P.: Vast-scale outdoor navigation using adaptive relative bundle adjustment. Int. J. Robot. Res. 29(8), 958–980 (2010)
Trullier, O., Wiener, S.I., Berthoz, A., Meyer, J.A.: Biologically based artificial navigation systems: review and prospects. Prog. Neurobiol. 51(5), 483–544 (1997)
Valencia, R., Andrade-Cetto, J., Porta, J.M.: Path planning in belief space with pose SLAM. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 78–83 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bender, D., Koch, W., Cremers, D. (2018). SLAM-Based Return to Take-Off Point for UAS. In: Lee, S., Ko, H., Oh, S. (eds) Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. MFI 2017. Lecture Notes in Electrical Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-319-90509-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-90509-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-90508-2
Online ISBN: 978-3-319-90509-9
eBook Packages: EngineeringEngineering (R0)