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Autonomous Robots

, 27:431 | Cite as

Using virtual scans for improved mapping and evaluation

  • Rolf Lakaemper
  • Nagesh Adluru
Article

Abstract

In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses (‘Virtual Scans’) about ideal models of objects in the robot’s environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated ‘Virtual Scans’ with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.

Keywords

Robot Mapping Evaluation of Mapping Algorithms Sparse scan alignment Spatial cognition in mapping Force fields 

References

  1. Adluru, N., Latecki, L. J., Lakämper, R., & Madhavan, R. (2006). Robot mapping for rescue robots. In Proc. of the IEEE Int. workshop on safety, security and rescue robotics (SSRR), Gaithersburg, Maryland, USA. Google Scholar
  2. Bertel, S., Barkowsky, T., Engel, D., & Freksa, C. (2006). Computational modeling of reasoning with mental images: Basic requirements. In Proceedings of the 7th international conference on cognitive modeling (pp. 50–55). Google Scholar
  3. Besl, P., & McKay, N. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256. CrossRefGoogle Scholar
  4. Birk, A., & Carpin, S. (2006). Merging occupancy grid maps from multiple robots. Proceedings of the IEEE, 94, 1384–1397. CrossRefGoogle Scholar
  5. Chang, H. J., Lee, C. S. G., Lu, Y. H., & Hu, Y. C. (2007). P-SLAM: Simultaneous localization and mapping with environmental-structure prediction. IEEE Transactions on Robotics, 23(2), 281–293. CrossRefGoogle Scholar
  6. Dissanayake, G., Durrant-Whyte, H., & Bailey, T. (2000). A computationally efficient solution to the simultaneous localization and map building (slam) problem. In Proceedings of the IEEE int. conference on robotics & automation (ICRA) (pp. 1009–1014). Google Scholar
  7. Doucet, A., de Freitas, N., & Gordon, N. (2001). Sequential Monte Carlo methods in practice. Berlin: Springer. zbMATHGoogle Scholar
  8. Eric, W., & Grimson, L. (1990). Object recognition by computer: the role of geometric constraints. Cambridge: MIT Press. Google Scholar
  9. Field, D. J., Hayes, A., & Hess, R. (1993). Contour integration by the human visual system: evidence for a local association field. Vision Research, 33, 173–193. CrossRefGoogle Scholar
  10. Freksa, C., Knauff, M., Krieg-Brckner, B., Nebel, B., & Barkowsky, T. (2004). Spatial cognition IV, reasoning, action, interaction. Berlin: Springer. Google Scholar
  11. Ghiselli-Crippa, T., Hirtle, S. C., & Munro, P. (1996). Connectionist models in spatial cognition (Vol. 32, pp. 87–104). Dordrecht: Springer. Google Scholar
  12. Grisetti, G., Stachniss, C., & Burgard, W. (2005). Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In Proceedings of the IEEE int. conference on robotics & automation (ICRA) (pp. 2443–2448). Google Scholar
  13. Hough, P. V. C. (1962). Methods and means for recognizing complex patterns. United States Patent 3069654. Google Scholar
  14. Huang, S., & Dissanayake, G. (2006). Convergence analysis for extended Kalman filter based slam. In Proceedings of the IEEE int. conference on robotics & automation (ICRA) (pp. 412–417). Google Scholar
  15. Knill, D. C., & Richards, W. (Eds.) (1996). Perception as Bayesian inference. New York: Cambridge University Press. zbMATHGoogle Scholar
  16. Kruijff, G. J. M., Zender, H., Jensfelt, P., & Christensen, H. I. (2007). Situated dialogue and spatial organization: what, where and why? International Journal of Advanced Robotic Systems, 4(1), 125–138. Google Scholar
  17. Kuipers, B. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119, 191–233. zbMATHCrossRefMathSciNetGoogle Scholar
  18. Kuipers, B. J. (1983). The cognitive map: could it have been any other way? In H. L. Pick & L. P. Acredolo (Eds.), Spatial orientation: theory, research, and application (pp. 345–360). New York: Plenum. Google Scholar
  19. Lagunovsky, D., & Ablameyko, S. (1997). Fast line and rectangle detection by clustering and grouping. In CAIP’97: Proceedings of the 7th international conference on computer analysis of images and patterns (pp. 503–510). London: Springer. Google Scholar
  20. Lakaemper, R., & Adluru, N. (2008). Map merging for distributed robot navigation. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2915–2921). Google Scholar
  21. Lakaemper, R., Latecki, L. J., Sun, X., & Wolter, D. (2005). Geometric robot mapping (Vol. 3429, pp. 11–22). Berlin: Springer. Google Scholar
  22. Lakaemper, R., Adluru, N., & Latecki, L. J. (2007a). Force field based n-scan alignment. In European conference on mobile robots, Freiburg, Germany. Google Scholar
  23. Lakaemper, R., Adluru, N., Latecki, L. J., & Madhavan, R. (2007b). Multi robot mapping using force field simulation: research articles. Journal of Field Robot, 24(8–9), 747–762. doi: 10.1002/rob.v24:8/9. CrossRefGoogle Scholar
  24. Lakaemper, R., Nüchter, A., Adluru, N., & Latecki, L. J. (2007c). Performance of 6d LuM and FFS SLAM: an example for comparison using grid and pose based evaluation methods. In Workshop on performance metrics and intelligent systems (PerMIS), Gaithersburg, MD. Google Scholar
  25. Latecki, L. J., Sobel, M., & Lakaemper, R. (2006). New EM derived from Kullback-Leibler divergence. In ACM SIGKDD int. conf. on knowledge discovery and data mining. Google Scholar
  26. Lu, F., & Milios, E. (1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4), 333–349. doi: 10.1023/A:1008854305733. CrossRefGoogle Scholar
  27. Martinelli, A., Tapus, A., Arras, K., & Siegwart, R. (2003). Multi-resolution slam for real world navigation. In Proceedings of the 11th international symposium of robotics research. Google Scholar
  28. NIST (2008). NIST response robot evaluation exercise. Search and rescue: Texas Engineering Extension Service (TEEX), http://www.teex.com/teex.cfm?pageid=USARprog&area=usar&templateid=1538.
  29. Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H., Pervölz, K., Hennig, M., Tiruchinapalli, K. R., Worst, R., & Christaller, T. (2005). Mapping of rescue environments with kurt3d. In Proc. of the IEEE int. workshop on safety, security and rescue robotics (SSRR), Kobe, Japan. Google Scholar
  30. Pentland, A. P. (1986). Perceptual organization and the representation of natural form. Artificial Intelligence, 28(3), 293–331. doi: 10.1016/0004-3702(86)90052-4. CrossRefMathSciNetGoogle Scholar
  31. Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L. E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., & Mahoney, P. (2006). Stanley: The robot that won the darpa grand challenge: Research articles. Journal of Robotic Systems, 23(9), 661–692. doi: 10.1002/rob.v23:9. Google Scholar
  32. Uttal, D. H. (2000). Seeing the big picture: Map use and the development of spatial cognition. Developmental Science, 3(3), 247–264. CrossRefGoogle Scholar
  33. Varsadan, I., Birk, A., & Pfingsthorn, M. (2008). Determining map quality through an image similarity metric. In Proceedings of the RoboCup symposium. Google Scholar
  34. Vasudevan, S., Nguyen, V., & Siegwart, R. (2006). Towards a cognitive probabilistic representation of space for mobile robots. In IEEE international conference on information acquisition (pp. 353–359). Google Scholar
  35. Yeap, W., & Jefferies, M. (2000). On early cognitive mapping. Spatial Cognition and Computation, 2(2), 85–116. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Temple UniversityPhiladelphiaUSA
  2. 2.University of WisconsinMadisonUSA

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