Abstract
The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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References
Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: Speeded up robust features. In Proc. of the European conf. on computer vision (ECCV).
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.
Chen, C., & Wang, H. (2007). Large-scale loop-closing by fusing range data and aerial image. International Journal of Robotics and Automation, 22(2), 160–169.
Davis, T. A. (2006). Direct methods for sparse linear systems. SIAM book series on the fundamentals of algorithms. Philadelphia: SIAM.
Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (1998). Monte Carlo localization for mobile robots. In Proc. of the IEEE int. conf. on robotics & automation (ICRA).
Ding, M., Lyngbaek, K., & Zakhor, A. (2008). Automatic registration of aerial imagery with untextured 3d lidar models. In Proc. of the IEEE conf. on computer vision and pattern recognition (CVPR).
Dogruer, C. U., Bugra, K. A., & Dolen, M. (2007). Global urban localization of outdoor mobile robots using satellite images. In Proc. of the int. conf. on intelligent robots and systems (IROS).
Doucet, A., de Freitas, N., & Gordan, N. (Eds.) (2001). Sequential Monte-Carlo methods in practice. Berlin: Springer.
Eustice, R., Singh, H., & Leonard, J. J. (2005). Exactly sparse delayed-state filters. In Proc. of the IEEE int. conf. on robotics & automation (ICRA) (pp. 2428–2435).
Foley, J. D., Van Dam, A., Feiner, K., Hughes, J. F., & Phillips, R. L. (1993). Introduction to computer graphics. Reading: Addison-Wesley.
Frese, U., Larsson, P., & Duckett, T. (2005). A multilevel relaxation algorithm for simultaneous localisation and mapping. IEEE Transactions on Robotics, 21(2), 1–12.
Früh, C., & Zakhor, A. (2004). An automated method for large-scale, ground-based city model acquisition. International Journal of Computer Vision, 60, 5–24.
Grisetti, G., Stachniss, C., & Burgard, W. (2005). Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In Proc. of the IEEE int. conf. on robotics & automation (ICRA) (pp. 2443–2448).
Grisetti, G., Stachniss, C., & Burgard, W. (2009, in press). Non-linear constraint network optimization for efficient map learning. IEEE Transactions on Intelligent Transportation Systems.
Gutmann, J.-S., & Konolige, K. (1999). Incremental mapping of large cyclic environments. In Proc. of the IEEE int. symposium on computational intelligence in robotics and automation (CIRA).
Howard, A. (2004). Multi-robot mapping using manifold representations. In Proc. of the IEEE int. conf. on robotics & automation (ICRA) (pp. 4198–4203).
Julier, S., Uhlmann, J., & Durrant-Whyte, H. (1995). A new approach for filtering nonlinear systems. In Proc. of the american control conference (pp. 1628–1632).
Korah, T., & Rasmussen, C. (2004). Probabilistic contour extraction with model-switching for vehicle localization. In IEEE intelligent vehicles symposium (pp. 710–715).
Kümmerle, R., Steder, B., Dornhege, C., Kleiner, A., Grisetti, G., & Burgard, W. (2009). Large scale graph-based SLAM using aerial images as prior information. In Proc. of robotics: science and systems (RSS), Seattle, WA, USA, June 2009.
Lee, K. W., Wijesoma, S., & Guzmán, J. I. (2007). A constrained slam approach to robust and accurate localisation of autonomous ground vehicles. Robotics and Autonomous Systems, 55(7), 527–540.
Leonard, J. J., & Durrant-Whyte, H. F. (1991). Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation, 7(4), 376–382.
Leung, K. Y. K., Clark, C. M., & Huissoon, J. P. (2008). Localization in urban environments by matching ground level video images with an aerial image. In Proc. of the IEEE int. conf. on robotics & automation (ICRA).
Lu, F., & Milios, E. (1997). Globally consistent range scan alignment for environment mapping. Journal of Autonomous Robots, 4, 333–349.
Montemerlo, M., & Thrun, S. (2004). Large-scale robotic 3-d mapping of urban structures. In Proc. of the int. symposium on experimental robotic (ISER) (pp. 141–150).
Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2003). FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In Proc. of the int. conf. on artificial intelligence (IJCAI) (pp. 1151–1156).
Olson, E. (2008). Robust and efficient robotic mapping. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, USA.
Olson, E., Leonard, J., & Teller, S. (2006). Fast iterative optimization of pose graphs with poor initial estimates. In Proc. of the IEEE int. conf. on robotics & automation (ICRA) (pp. 2262–2269).
Parsley, M., & Julier, S. J. (2009). Slam with a heterogeneous prior map. In SEAS-DTC conference.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical Recipes (2nd edn.). Cambridge: Cambridge University Press.
Smith, R., Self, M., & Cheeseman, P. (1990). Estimating uncertain spatial relationships in robotics. In I. Cox & G. Wilfong (Eds.), Autonomous robot vehicles (pp. 167–193). Berlin: Springer.
Sofman, B., Ratliff, E. L., Bagnell, J. A., Vandapel, N., & Stentz, T. (2006). Improving robot navigation through self-supervised online learning. In Proc. of robotics: science and systems (RSS), August 2006.
Thrun, S., Liu, Y., Koller, D., Ng, A. Y., Ghahramani, Z., & Durrant-Whyte, H. (2004). Simultaneous localization and mapping with sparse extended information filters. International Journal of Robotics Research, 23(7/8), 693–716.
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.
Uhlmann, J. (1995). Dynamic map building and localization: new theoretical foundations. PhD thesis, University of Oxford.
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Kümmerle, R., Steder, B., Dornhege, C. et al. Large scale graph-based SLAM using aerial images as prior information. Auton Robot 30, 25–39 (2011). https://doi.org/10.1007/s10514-010-9204-1
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DOI: https://doi.org/10.1007/s10514-010-9204-1