Using virtual scans to improve alignment performance in robot mapping
Pages 265 - 270
Abstract
We present a concept and implementation of a system to integrate low level and mid level spatial cognition processes for an application in robot mapping. Feedback between the two processes helps to improve performance of the recognition task, in our example the alignment of laser scans. The low level laser range scan data ('real scans'), are analyzed with respect to mid level geometric structures. The analysis leads to generation of hypotheses (Virtual Scans) about existing real world objects. These hypotheses are used to augment the real scan data. The core mapping process, called Force Field Simulation, iteratively aligns the augmented data set which then in turn is re analyzed to confirm, modify, or discard the hypotheses in each iteration. Experiments with scan data from a Rescue Robot Scenario show the applicability and advantages of the approach.
References
[1]
S. Bertel. Thomas Barkowsky, Dominik Engel, Christian Freksa. Computational modeling of reasoning with mental images: basic requirements. D. Fum, F. del Missier, A. Stocco (Eds.), Proceedings of the 7th International Conference on Cognitive Modeling ICCM06, 2006.
[2]
P. Besl. N. mckay. a method for registration of 3.d shapes. IEEE PAMI, 14(2), 1992.
[3]
H. J. Chang. C. s. george lee and y. h. lu and y. c. hu. p-slam: Simultaneous localization and mapping with environmental structure prediction. IEEE Transactions on Robotics, Vol. 23, No. 2, April 2007.
[4]
G. Dissanayake. H. durrant-whyte and t. bailey. a computationally efficient solution to the simultaneous localization and map building (slam) problem. ICRA2000 Workshop on Mobile Robot Navigation and Mapping, 2000.
[5]
A. Doucet. N. defreitas, n. gordon. sequential monte carlo methods in practice. Springer, 2000.
[6]
D. J. Field. A. hayes, r. f. hess. contour integration by the human visual system: evidence for a local association field. Vision Research, 33, pages 173--193, 1993.
[7]
C. Freksa. M. knauff, b. krieg-brueckner, b. nebel, t. barkowsky. spatial cognition iv: Reasoning, action, interaction. Springer, 2004.
[8]
T. Ghiselli-Crippa. Hirtle s. c., munro, p. connectionist models in spatial cognition. The Construction of Cognitive Maps, Kluwer Academic Publishers. 1--51, pages 87--104, 1996.
[9]
W. Grimson. Object recognition by computer: The role of geometric constraints. Boston, MA: MIT Press, 1991.
[10]
G. Grisetti. C. stachniss, burgard w. improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. ICRA, 2005.
[11]
P. V. C. Hough. Methods and means for recognizing complex patterns. US patent 3,069,654, 1962.
[12]
S. Huang. G. dissanayake. convergence analysis for extended kalman filter based slam. IEEE International Conference on Robotics and Automation, 2006.
[13]
D. C. Knill. W. richards. perception as bayesian inference. New York: Cambridge, 1996.
[14]
G. J. M. Kruijff. H. zender, p. jensfelt, h. i. christensen. situated dialogue and spatial organization: What, whereĚ and why? Int. J. of Advanced Robotic Systems, 2007.
[15]
B. J. Kuipers. The cognitive map: 'could it have been any other way?'. Spatial Orientation: Theory, Research, and Application. New York: Plenum Press, pages 345--359, 1983.
[16]
B. J. Kuipers. The spatial semantic hierarchy. Artificial Intelligence 119, pages 191--233, 2000.
[17]
D. Lagunovsky. Ablameyko s. fast line and rectangle detection by clustering and grouping. Proc. of CAIP'97, Kiel, Germany, 1997.
[18]
R. Lakaemper. L. j. latecki, d. wolter. geometric robot mapping. Int. Conf. on Discrete Geometry for Computer Imagery (DGCI), 2005.
[19]
R. Lakaemper. A. nuechter, n. adluru. performance of 6d lum and ffs slam. Workshop on Performance Metrics and Intelligent Systems (PerMIS), Gaithersburg, MD, August 2007.
[20]
R. Lakaemper. N. adluru, l. j. latecki, r. madhavan. multi robot mapping using force field simulation. Journal of Field Robotics, Special Issue on Quantitative Performance Evaluation of Robotic and Intelligent Systems, 2007.
[21]
L. J. Latecki. R. lakaemper. polygonal approximation of laser range data based on perceptual grouping and em. IEEE Int. Conf. on Robotics and Automation (ICRA), Orlando, Florida, May 2006.
[22]
F. Lu. Milios, e. globally consistent range scan alignment for environment mapping. Auton. Robots, 4(4), pages 333--349, 1997.
[23]
A. Martinelli. A. tapus, k. o. arras, r. siegwart. multi-resolution slam for real world navigation. Proceedings of the 11th International Symposium of Robotics Research, Siena, Italy, 2003.
[24]
A. Nuechter. K. lingemann, j. hertzberg, h. surmann, k. pervoelz, m. hennig, k. r. tiruchinapalli, r. worst, t. christaller. mapping of rescue environments with kurt3d. Proceedings of the International Workshop on Safety, Security and Rescue Robotics (SSRR '05), Kobe, Japan, June 2005.
[25]
A. Pentland. Perceptual organization and the representation of natural form. AI Journal, Vol. 28, No. 2, pages 1--38, 1986.
[26]
e. a. Thrun. Stanley: The robot that won the darpa grand challenge. Journal of Field Robotics 23(9), pages 661--692, 2006.
[27]
D. Uttal. Seeing the big picture: Map use and the development of spatial cognition. Developmental Science, 3, pages 247--286, 2000.
[28]
S. Vasudevan. V. nguyen, r. siegwart. towards a cognitive probabilistic representation of space for mobile robots. Proceedings of the IEEE International Conference on Information Acquisition (ICIA), Shandong, China, pages 20--23, August 2006.
[29]
W. K. Yeap. M. e. jefferies. on early cognitive mapping. Spatial Cognition and Computation 2(2), pages 85--116, 2001.
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Published: 19 August 2008
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