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Interactive mapping using range sensor data under localization uncertainty

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Języki publikacji
EN
Abstrakty
EN
Several methods have been proposed in the literature to address the problem of automatic mapping by a robot using range scan data, under localization uncertainty. Most scan matching methods rely on the minimization of the matching error among individual range scans. However, uncertainty in sensor data often leads to erroneous matching, hard to cope with in a purely automatic approach. This paper proposes a semi-automatic approach, denoted interactive mapping, involving a human operator in the process of detecting and correcting erroneous matches. Instead of allowing the operator complete freedom in correcting the matching in a frame by frame basis, the proposed method facilitates the adjustment along the directions with more ambiguity, while constraining the others. Experimental results using LIDAR data are presented to validate empirically the approach, together with a preliminary user study to evaluate the benefits of the approach.
Twórcy
autor
  • Institute for Systems and Robotics, Instituto Superior Técnico
autor
  • Institute for Systems and Robotics, Instituto Superior Técnico,
Bibliografia
  • [1] S. Thrun, “Robotic mapping:Asurvey”. In: Exploring artificial intelligence in the new millennium, G. Lakemeyer, B. Nebel (eds.), Morgan Kaufmann, 2002, pp. 1–35.
  • [2] A. Elfes, “Sonar-based real-world mapping and navigation”, IEEE J. Robot. Automat., vol. 3, no. 3, 1987, pp. 249–265.
  • [3] F. Lu, E. Milios, “Globally consistent range scan alignment for environment mapping”, Autonomous robots, vol. 4, no. 4, 1997, pp. 333–349.
  • [4] D. Murray, C. Jennings, “Stereo vision based mapping and navigation for mobile robots”. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA’97), vol. 2, April 1997, pp. 1694–1699.
  • [5] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, “RGB-D mapping: Using depth cameras for dense 3d modeling of indoor environments”. In: The 12th International Symposium on Experimental Robotics (ISER), 2010.
  • [6] A. Elfes, “Occupancy grids: A probabilistic framework for robot perception and navigation”, Ph.D. dissertation, Carnegie Mellon University, 1989.
  • [7] P. Newman, G. Sibley, M. Smith, M. Cummins, A. Harrison, C. Mei, I. Posner, R. Shade, D. Schroeter, D. Cole, I. Reid, “Navigating, recognising and describing urban spaces with vision and lasers”, The International Journal of Robotics Research, vol. 28, no. 11–12, 2009, pp. 1406–1433.
  • [8] J. Besl, N. D. McKay, “A method for registration of 3-d shapes”, IEEE Trans. Pattern Anal. Mach. Intell., 14(2), 1992, pp. 239–256.
  • [9] K. Lai, L. Bo, X. Ren, D. Fox, “A large-scale hierarchical multi-view RGB-D object dataset”. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA’11), 2011, pp. 1817–1824.
  • [10] F. Dellaert, D. Bruemmer, “Semantic slam for collaborative cognitive workspaces”. In: AAAI Fall Symposium Series 2004: Workshop on The Interaction of Cognitive Science and Robotics: From Interfaces to Intelligence, 2004.
  • [11] A. Diosi, G. Taylor, L. Kleeman, “Interactive SLAM using laser and advanced sonar”. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA’05), 2005, pp. 1103–1108.
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Bibliografia
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