Abstract
Providing a robot with a fully detailed map is one appealing key for the Simultaneous Localisation and Mapping (SLAM) problem. It gives the robot a lot of hints to solve either the data association or the localisation problem itself. The more details are in the map, the more chances are that different places may appear differently, solving ambiguities. The more landmarks are used, the more accurate are the algorithms that solve the localisation problem since in a least square sense an approximation of the solution is more precise. Last, it helps a lot in the presence of a few dynamic objects because these moving parts of the environment remain marginal in the amount of data used to model the map and can thus be filtered out. For instance, the moving objects can be detected or cancelled in the localisation procedure by robust techniques using Monte-Carlo algorithms [6] or RANSAC [4].
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References
Biber, P., Straßer, W.: The normal distributions transform: A new approach to laser scan matching. In: IEEE/RJS International Conference on Intelligent Robots and Systems (2003)
Cole, D., Newman, P.: Using laser range data for 3d slam in outdoor environments. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Florida (2006)
Elfes, A.: Occupancy grids: a probabilistic framework for robot perception and navigation. PhD thesis, Carnegie Mellon University (1989)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2443–2448 (2005)
Khan, Z., Balch, T., Dellaert, F.: Mcmc data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 1960–1972 (2006)
Kraetzschmar, G.K., Gassull, G.P., Uhl, K.: Probabilistic quadtrees for variable-resolution mapping of large environments. In: Ribeiro, M.I., Victor, J.S. (eds.) Proceedings of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles (July 2004)
Payeur, P., Hébert, P., Laurendeau, D., Gosselin, C.: Probabilistic octree modeling of a 3-d dynamic environment. In: Proc. IEEE ICRA 1997, Albuquerque, NM, April 20-25, 1997, pp. 1289–1296 (1997)
Payeur, P., Laurendeau, D., Gosselin, C.: Merging uncertainty into probabilistic octree models of 3-d perturbed workspaces. In: Proc. VI 1998, Vancouver BC, June 18-20, 1998, pp. 439–446 (1998)
Payeur, P., Laurendeau, D., Gosselin, C.: Range data merging for probabilistic octree modeling of 3-d workspaces. In: Proc. ICRA 1998, Leuven, Belgium, May 16-21, 1998, pp. 3071–3078 (1998)
Stachniss, C.: Corrected robotic log-files, http://www.informatik.uni-freiburg.de/stachnis/datasets.html
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), ch. 9, p. 231. The MIT Press, Cambridge (2005)
Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: Proc. of the International Conference on Intelligent Robots and Systems (IROS) (2006)
Vandorpe, J., Van Brussel, H., Xu, H.: Exact dynamic map building for a mobile robot using geometrical primitives produced by a 2d range finder. In: IEEE International Conference on Robotics and Automation, April 22-28, 1996, vol. 1, pp. 901–908 (1996)
Yguel, M., Aycard, O., Laugier, C.: Wavelet occupancy grids: a method for compact map building. In: Proc. of the Int. Conf. on Field and Service Robotics (2005)
Yguel, M., Keat, C.T.M., Braillon, C., Laugier, C., Aycard, O.: Dense mapping for range sensors: Efficient algorithms and sparse representations. In: Proceedings of Robotics: Science and Systems, Atlanta, GA, USA (June 2007)
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Yguel, M., Aycard, O., Laugier, C. (2008). Update Policy of Dense Maps: Efficient Algorithms and Sparse Representation . In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_3
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DOI: https://doi.org/10.1007/978-3-540-75404-6_3
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