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A quantitative assessment of structural errors in grid maps

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Abstract

Various common error sources affect the quality of a map, e.g., salt and pepper noise and other forms of noise that are more or less uniformly distributed over the map. But there also exist errors that only occur very rarely in the mapping process but that have severe effects on the final result. They influence not only the local accuracy but also the whole spatial layout of the map. Examples of related error sources include bump noise in the robot’s pose or residual errors in Simultaneous Localization and Mapping (SLAM). The concept of brokenness is introduced in this article to capture the notion of structural errors in grid maps. The map is partitioned into regions that are locally consistent with ground truth but “off” relative to each other. Brokenness measures the number of these regions and their spatial relations. A theoretical basis is introduced to derive the concept of brokenness in a formal way. Furthermore, it is shown how brokenness can be computed in an algorithmic way. Experiments with maps from simulated as well as real world data are presented. They show that the metric can indeed be used to automatically determine the structural quality of a map in a quantitative way.

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References

  • Alliney, S., & Morandi, C. (1986). Digital image registration using projections. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(2), 222–233.

    Article  Google Scholar 

  • Birk, A. (1996). Learning geometric concepts with an evolutionary algorithm. In Proceedings of the fifth annual conference on evolutionary programming. Cambridge: MIT Press.

    Google Scholar 

  • Birk, A., & Carpin, S. (2006). Merging occupancy grid maps from multiple robots. IEEE Proceedings, Special Issue on Multi-Robot Systems, 94(7), 1384–1397.

    Google Scholar 

  • Birk, A., Pathak, K., Schwertfeger, S., & Chonnaparamutt, W. (2006). The IUB Rugbot: an intelligent, rugged mobile robot for search and rescue operations. In IEEE international workshop on safety, security, and rescue robotics (SSRR). New York: IEEE Press.

    Google Scholar 

  • Blanco, J.-L., & Javier Gonzalez, J. A. F. M. (2007). A new method for robust and efficient occupancy grid-map matching. Pattern Recognition and Image Analysis, 4478, 194–201.

    Article  Google Scholar 

  • Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys, 24(4), 325–376.

    Article  Google Scholar 

  • Carpin, S. (2008a). Fast and accurate map merging for multi-robot systems. Autonomous Robots, 25(3), 305–316.

    Article  Google Scholar 

  • Carpin, S. (2008b). Merging maps via hough transform. In IEEE/RSJ international conference on Intelligent Robots and Systems (IROS 2008) (pp. 1878–1883).

  • Carpin, S., & Pillonetto, G. (2003a). Learning sample distribution for randomized robot motion planning: role of history size. In Proceedings of the 3rd international conference on artificial intelligence and applications (pp. 58–63). ACTA Press.

  • Carpin, S., & Pillonetto, G. (2003b). Robot motion planning using adaptive random walks. In Proceedings of the IEEE international conference on robotics and automation (pp. 3809–3814).

  • Carpin, S., & Pillonetto, G. (2005). Motion planning using adaptive random walks. IEEE Transactions on Robotics, 21(1), 129–136.

    Article  Google Scholar 

  • Carpin, S., Birk, A., & Jucikas, V. (2005). On map merging. International Journal of Robotics and Autonomous Systems, 53, 1–14.

    Article  Google Scholar 

  • Carpin, S., Birk, A., Lewis, M., & Jacoff, A. (2006). High fidelity tools for rescue robotics: results and perspectives. In I. Noda, A. Jacoff, A. Bredenfeld, & Y. Takahashi (Eds.), Lecture notes in artificial intelligence (LNAI). RoboCup 2005: robot soccer world cup IX. Berlin: Springer.

    Google Scholar 

  • Chandran-Ramesh, M., & Newman, P. (2008). Assessing map quality using conditional random fields. In C. Laugier & R. Siegwart (Eds.), Springer tracts in advanced robotics. Field and service robotics. Berlin: Springer.

    Google Scholar 

  • Dedeoglu, G., & Sukhatme, G. (2000). Landmark-based matching algorithm for cooperative mapping by autonomous robots. In Distributed autonomous robotic systems (Vol. 4, pp. 251–260). Berlin: Springer.

    Google Scholar 

  • Dorai, C., Wang, G., Jain, A. K., & Mercer, C. (1998). Registration and integration of multiple object views for 3d model construction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 83–89.

    Article  Google Scholar 

  • Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, 22(6), 46–57.

    Article  Google Scholar 

  • Fenwick, J., Newman, P., & Leonard, J. (2002). Cooperative concurrent mapping and localization. In Proceedings of the 2002 IEEE international conference on robotics and automation (ICRA). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Frese, U. (2006). A discussion of simultaneous localization and mapping. Autonomous Robots, 20, 25–42.

    Article  Google Scholar 

  • Gool, L. V., Moons, T., & Ungureanu, D. (1996). Affine/photometric invariants for planar intensity patterns. In Proceedings of European conference on computer vision.

  • 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 international conference on robotics and automation (ICRA).

  • Gutmann, J. S., Burgard, W., Fox, D., & Konolige, K. (1998). An experimental comparison of localization methods. In IEEE/RSJ International conference on intelligent robots and systems (IROS) (Vol. 2, pp. 736–743).

  • Howard, A. (2004). Multi-robot mapping using manifold representations. In Proceedings of the IEEE international conference on robotics and automation (pp. 4198–4203).

  • Howard, A., & Roy, N. (2003). The robotics data set repository (Radish). http://radish.sourceforge.net/.

  • Huttenlocher, D. P., & Rucklidge, W. J. (1993). A multi-resolution technique for comparing images using the Hausdorff distance. In Proceedings computer vision and pattern recognition (pp. 705–706), New York, NY.

  • Ko, J., Stewart, B., Fox, D., Konolige, K., & Limketkai, B. (2003). A practical, decision-theoretic approach to multi-robot mapping and exploration. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS).

  • Lee, D. C. (2003). The map-building and exploration strategies of a simple sonar-equipped mobile robot: an experimental, quantitative evaluation. Distinguished dissertations in computer science. Cambridge: Cambridge University Press.

    Google Scholar 

  • Leonard, J. J., & Durrant-Whyte, H. F. (1992). Directed sonar sensing for mobile robot navigation. Berlin: Springer.

    MATH  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Madhavan, R., Fregene, K., & Parker, L. (2004). Terrain aided distributed heterogenous multirobot localization and mapping. Autonomous Robots, 17, 23–39.

    Article  Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2003). A performance evaluation of local descriptors. In Proceedings of computer vision and pattern recognition.

  • Moravec, M. B. (1993). Learning sensor models for evidence grids. CMU Robotics Institute 1991 annual research review (pp. 8–15).

  • Moravec, H., & Elfes, A. (1985). High resolution maps from wide angle sonar. In Proceedings of the IEEE international conference on robotics and automation (pp. 116–121).

  • Roy, N., & Dudek, G. (2001). Collaborative exploration and rendezvous: algorithms, performance bounds and observations. Autonomous Robots, 11.

  • Rucklidge, W. J. (1997). Efficiently locating objects using the Hausdorff distance. International Journal of Computer Vision, 24(3), 251–270.

    Article  Google Scholar 

  • Scrapper, C., Madhavan, R., & Balakirsky, S. (2007). Stable navigation solutions for robots in complex environments. In IEEE international workshop on safety, security and rescue robotics (SSRR) (pp. 1–6).

  • Scrapper, C., Madhavan, R., & Balakirsky, S. (2008). Performance analysis for stable mobile robot navigation solutions. In Proceedings of SPIE. International Society for Optical Engineering.

  • Stricker, D. (2001). Tracking with reference images: a real-time and markerless tracking solution for out-door augmented reality applications. In Proceedings of the 2001 conference on virtual reality, archeology, and cultural heritage (pp. 77–82). New York: ACM.

    Chapter  Google Scholar 

  • Thrun, S. (2001). A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5), 335–363.

    Article  Google Scholar 

  • Thrun, S. (2002). Robotic mapping: a survey. In G. Lakemeyer & B. Nebel (Eds.), Exploring artificial intelligence in the new millennium. San Mateo: Morgan Kaufmann.

    Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2000). A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping. In Proceedings of the IEEE international conference on robotics and automation (pp. 321–328).

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • USARsim (2006). Unified system for automation and robotics simulator (USARsim). http://usarsim.sourceforge.net/.

  • Varsadan, I., Birk, A., & Pfingsthorn, M. (2009). Determining map quality through an image similarity metric. In L. Iocchi, H. Matsubara, A. Weitzenfeld, & C. Zhou (Eds.), Lecture notes in artificial intelligence. RoboCup 2008: robot WorldCup XII. Berlin: Springer.

    Google Scholar 

  • Williams, S., Dissanayake, G., & Durrant-Whyte, H. (2002). Towards multi-vehicle simultaneous localisation and mapping. In Proceedings of the 2002 IEEE international conference on robotics and automation (ICRA). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Wnuk, K. (2005). Dense 3d mapping with monocular vision.

  • Yairi, T. (2004). Covisibility based map learning method for mobile robots. In 8th pacific Rim international conference on artificial intelligence.

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  1. Jacobs University, Campus Ring 1, 28759, Bremen, Germany

    Andreas Birk

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  1. Andreas Birk
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Correspondence to Andreas Birk.

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Birk, A. A quantitative assessment of structural errors in grid maps. Auton Robot 28, 187–196 (2010). https://doi.org/10.1007/s10514-009-9159-2

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  • DOI: https://doi.org/10.1007/s10514-009-9159-2

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