Abstract
This paper demonstrates how a heterogeneous fleet of unmanned aerial vehicles (UAVs) can support human operators in search and rescue (SaR) scenarios. We describe a fully autonomous delegation framework that interprets the top-level commands of the rescue team and converts them into actions of the UAVs. In particular, the UAVs are requested to autonomously scan a search area and to provide the operator with a consistent georeferenced 3D reconstruction of the environment to increase the environmental awareness and to support critical decision-making. The mission is executed based on the individual platform and sensor capabilities of rotary- and fixed-wing UAVs (RW-UAV and FW-UAV respectively): With the aid of an optical camera, the FW-UAV can generate a sparse point-cloud of a large area in a short amount of time. A LiDAR mounted on the autonomous helicopter is used to refine the visual point-cloud by generating denser point-clouds of specific areas of interest. In this context, we evaluate the performance of point-cloud registration methods to align two maps that were obtained by different sensors. In our validation, we compare classical point-cloud alignment methods to a novel probabilistic data association approach that specifically takes the individual point-cloud densities into consideration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Due to space constraints in this publication, only a subset of the data can be presented. However, the datasets can be requested from the authors.
- 2.
For more details about the state estimation framework we refer to [8].
- 3.
The vision point-clouds are generated using the commercial software pix4d.
References
Oettershagen, P., Stastny, T., Mantel, T., Melzer, A., Rudin, K., Gohl, P., Agamennoni, G., Alexis, K., Siegwart, R.: Long-Endurance Sensing and Mapping Using a Hand-Launchable Solar-Powered UAV. In: Wettergreen, D.S., Barfoot, T.D. (eds.) Field and Service Robotics. STAR, vol. 113, pp. 441–454. Springer, Heidelberg (2016). doi:10.1007/978-3-319-27702-8_29
Conte, G., Rudol, P., Doherty, P.: Evaluation of a light-weight lidar and a photogrammetric system for unmanned airborne mapping applications. PFG Photogrammetrie, Fernerkundung, Geoinformation 2014, 287–298 (2014)
Agamennoni, G., Fontana, S., Siegwart, R.Y., Sorrenti, D.G.: Point clouds registration with probabilistic data association. In: Proceedings of the International Conference on Intelligent Robots and Systems. IEEE (2016)
Doherty, P., Kvarnström, J., Rudol, P., Wzorek, M., Conte, G., Berger, C., Hinzmann, T., Stastny, T.: A Collaborative Framework for 3D Mapping Using Unmanned Aerial Vehicles. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (eds.) PRIMA 2016. LNCS (LNAI), vol. 9862, pp. 110–130. Springer, Heidelberg (2016). doi:10.1007/978-3-319-44832-9_7
Doherty, P., Heintz, F., Kvarnström, J.: High-level mission specification and planning for collaborative unmanned aircraft systems using delegation. Unmanned Syst. 1(1), 75–119 (2013)
Skaloud, J.: Optimizing georeferencing of airborne survey systems by INS/DGPS. Ph.D. thesis, University of Calgary, Alberta (1999)
Britting, K.R.: Inertial Navigation Systems Analysis. Wiley, New York (1971)
Leutenegger, S., Melzer, A., Alexis, K., Siegwart, R.: Robust state estimation for small unmanned airplanes. In: 2014 IEEE Conference on Control Applications (CCA), pp. 1003–1010, October 2014
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)
Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10, 145–155 (1992)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13, 119–152 (1994)
Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets. Auton. Robots 34, 133–148 (2013)
Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Proceedings of Robotics: Science and Systems, Seattle, USA, June 2009
Biber, P., Strasser, W.: The normal distributions transform: a new approach to laser scan matching. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2003), vol.3, pp. 2743-2748 (2003). doi:10.1109/IROS.2003.1249285
Doherty, P., Kvarnström, J., Wzorek, W., Rudol, P., Heintz, F., Conte, G.: HDRC3 - a distributed hybrid deliberative/reactive architecture for unmanned aircraft systems. In: Valavanis, K.P., Vachtsevanos, G.J. (eds.) Handbook of Unmanned Aerial Vehicles, pp. 849–952. Springer, Heidelberg (2014)
Vempati, A.S., Agamennoni, G., Stastny, T., Siegwart, R.: Victim detection from a fixed-wing UAV: experimental results. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Pavlidis, I., Feris, R., McGraw, T., Elendt, M., Kopper, R., Ragan, E., Ye, Z., Weber, G. (eds.) ISVC 2015. LNCS, vol. 9474, pp. 432–443. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27857-5_39
Acknowledgment
The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n\(^\circ \)285417 (ICARUS) and n\(^\circ \)600958 (SHERPA). Furthermore, the authors want to thank Gabriel Agamennoni and Simone Fontana for providing an initial implementation of the probabilistic data association algorithm.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hinzmann, T. et al. (2017). Collaborative 3D Reconstruction Using Heterogeneous UAVs: System and Experiments. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-50115-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50114-7
Online ISBN: 978-3-319-50115-4
eBook Packages: EngineeringEngineering (R0)