Abstract
Learning maps from sensor data has been addressed since more than two decades by Simultaneous Localization and Mapping (SLAM) systems. Modern state-of-the-art SLAM approaches exhibit excellent performances and are able to cope with environments having the scale of a city. Usually these methods are entailed for on-line operation, requiring the data to be acquired in a single run, which is not always easy to obtain. To gather a single consistent map of a large environment we therefore integrate data acquired in multiple runs. A possible solution to this problem consists in merging different submaps. The literature proposes several approaches for map merging, however very few of them are able to operate with local maps affected by inconsistencies. These methods seek to find the global arrangement of a set of rigid bodies, that maximizes some overlapping criterion. In this paper, we present an off-line technique for merging maps affected by residual errors into a single consistent global map. Our method can be applied in combination with existing map merging approaches, since it requires an initial guess to operate. However, once this initial guess is provided, our method is able to substantially lessen the residual error in the final map. We validated our approach on both real world and simulated datasets to refine solutions of traditional map merging approaches.
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References
Agarwal, P., Tipaldi, G.D., Spinello, L., Stachniss, C., Burgard, W.: Robust map optimization using dynamic covariance scaling. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (May 2013)
Blanco, J.L., Fernández-Madrigal, J.A., Gonzalez, J.: An entropy-based measurement of certainty in rao-blackwellized particle filter mapping. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3550–3555. IEEE (2006)
Bunke, H.: Graph matching: Theoretical foundations, algorithms, and applications. In: Proc. Vision Interface, vol. 2000, pp. 82–88 (2000)
Carlone, L., Ng, M.K., Du, J., Bona, B., Indri, M.: Rao-blackwellized particle filters multi robot slam with unknown initial correspondences and limited communication. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 243–249. IEEE (2010)
Carpin, S.: Fast and accurate map merging for multi-robot systems. Autonomous Robots 25(3), 305–316 (2008)
Cunningham, A., Paluri, M., Dellaert, F.: Ddf-sam: Fully distributed slam using constrained factor graphs. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3025–3030. IEEE (2010)
Erinc, G., Carpin, S.: Anytime merging of appearance-based maps. Autonomous Robots 36(3), 241–256 (2014)
Grisetti, G., Kümmerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based slam. Magazine on Intelligent Transportation Systems 2(4), 31–43 (2010)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. on Robotics 23(1), 34–46 (2007)
Howard, A., Roy, N.: The robotics data set repository, Radish (2003), http://radish.sourceforge.net/
Huang, W.H., Beevers, K.R.: Topological map merging. The International Journal of Robotics Research 24(8), 601–613 (2005)
Jennings, J., Kirkwood-Watts, C., Tanis, C.: Distributed map-making and navigation in dynamic environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 1695–1701. IEEE (1998)
Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) (2011)
Lazaro, M., Paz, L., Piniés, P., Castellanos, J., Grisetti, G.: Multi-robot slam using condensed measurements. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1069–1076. IEEE (2013)
Leung, K.Y.K., Barfoot, T.D., Liu, H.H.: Distributed and decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3841–3847. IEEE (2011)
Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Autonomous Robots 4, 333–349 (1997)
Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems 18(3), 249–275 (1997)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to simultaneous localization and mapping. In: Proc. of the National Conference on Artificial Intelligence (AAAI), Edmonton, Canada, pp. 593–598 (2002)
Olson, E., Leonard, J., Teller, S.: Fast iterative optimization of pose graphs with poor initial estimates. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 2262–2269 (2006)
Saeedi, S., Paull, L., Trentini, M., Seto, M., Li, H.: Map merging using hough peak matching. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4683–4688. IEEE (2012)
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial realtionships in robotics. In: Cox, I., Wilfong, G. (eds.) Autonomous Robot Vehicles, pp. 167–193. Springer (1990)
Thrun, S., Liu, Y., Koller, D., Ng, A., Ghahramani, Z., Durrant-Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. Int. Journal of Robotics Research 23(7/8), 693–716 (2004)
Wulf, O., Nuchter, A., Hertzberg, J., Wagner, B.: Ground truth evaluation of large urban 6d slam. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pp. 650–657. IEEE (2007)
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Bonanni, T.M., Grisetti, G., Iocchi, L. (2014). Merging Partially Consistent Maps. In: Brugali, D., Broenink, J.F., Kroeger, T., MacDonald, B.A. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2014. Lecture Notes in Computer Science(), vol 8810. Springer, Cham. https://doi.org/10.1007/978-3-319-11900-7_30
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DOI: https://doi.org/10.1007/978-3-319-11900-7_30
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