Abstract
In this paper, we explore the use of synthesized landmark maps for absolute localization of a smart wheelchair system outdoors. In this paradigm, three-dimensional map data are acquired by an automobile equipped with high precision inertial/GPS systems, in conjunction with light detection and ranging (LIDAR) systems, whose range measurements are subsequently registered to a global coordinate frame. The resulting map data are then synthesized a priori to identify robust, salient features for use as landmarks in localization. By leveraging such maps with landmark meta-data, robots possessing far lower cost sensor suites gain many of the benefits obtained from the higher fidelity sensors, but without the cost.We show that by using such a map-based localization approach, a smart wheelchair system outfitted only with a 2-D LIDAR and encoders was able to maintain accurate, global pose estimates outdoors over almost 1 km paths.
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References
Gao, C., Hoffman, I., Miller, T., Panzarella, T., Spletzer, J.: Autonomous Docking of a Smart Wheelchair for the Automated Transport and Retrieval System (ATRS). Journal of Field Robotics 25(4-5), 203–222 (2008)
Buehler, M., Iagnemma, K., Singh, S.: Special issues on the 2007 darpa urban challenge. The Journal of Field Robotics 25(8-10) (August-October 2008)
Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. on Robotics and Automation 17(3), 229–241 (2001)
Newman, P., Leonard, J.: Pure range-only subsea slam. In: IEEE International Conference on Robotics and Automation, Taiwan, China (September 2003)
Masson, F., Nieto, J., Guivant, J., Nebot, E.: Robust autonomous navigation and world representation in outdoor environments. Mobile Robots Perception and Navigation, 299–320 (2007)
United Nations, Population Division: World Urbanization Prospects (February 2008)
Zhao, H., Shibasaki, R.: Reconstructing textured cad model of urban environment using vehicle borne laser range scanners and line cameras. In: Second International Workshop on Computer Vision System (ICVS), Vancouver, Canada, pp. 284–295 (2001)
Frueh, C., Zakhor, A.: Reconstructing 3d city models by merging ground-based and airborne views. In: GarcÃa, N., Salgado, L., MartÃnez, J.M. (eds.) VLBV 2003. LNCS, vol. 2849, pp. 306–313. Springer, Heidelberg (2003)
Georgiev, A., Allen, P.: Localization Methods for a Mobile Robot in Urban Environments. IEEE Trans. on Robotics 20(5), 851–864 (2004)
Howard, A., Wolf, D.F., Sukhatme, G.S.: Towards 3d mapping in large urban environments. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Sendai, Japan, September 2004, pp. 419–424 (2004)
Jensen, B., Weingarten, J., Kolski, S., Siegwart, R.: Laser range imaging using mobile robots: From pose estimation to 3d-models. In: 1st Range Imaging Research Day, Zürich, Switzerland, September 2005, pp. 129–144 (2005)
Borrmann, D., Elseberg, J., Lingemann, K., Nüchter, A., Hertzberg, J.: Globally consistent 3d mapping with scan matching. Journal of Robotics and Autonomous Systems (JRAS) 56(2), 130–142 (2008)
Educating Silicon, Google Street View - Soon in 3D? (April 2008), http://www.educatingsilicon.com/
Altermatt, M., Martinelli, A., Tomatis, N., Siegwart, R.: SLAM with Corner Features Based on a Relative Map. In: IEEE/RSJ IROS (October 2004)
Kingston, T., Laflamme, C.: Automated road sign detection and recognition. Journal of the International Municipal Signal Association, 46–49 (January/February 2007)
Ramos, F., Nieto, J., Durrant-Whyte, H.: Recognising and Modelling Landmarks to Close Loops in Outdoor SLAM. In: IEEE ICRA (March 2007)
Nieto, J., Bailey, T., Nebot, E.: Recursive scan-matching SLAM. Robotics and Autonomous Systems 55, 39–49 (2007)
Bohren, J., Derenick, J., Foote, T., Keller, J., Kushleyev, A., Lee, D., Satterfield, B., Spletzer, J., Stewart, A., Vernaza, P.: Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge. Journal of Field Robotics (2008) (accepted for publication)
Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM (1981)
Thrun, S.: Robotic mapping: A survey. Tech. Rep. CMU-CS-02-111, Carnegie Mellon University (2002)
Intel Corporation, OpenCV, http://sourceforge.net/projects/opencv/
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Gao, C., Sands, M., Spletzer, J.R. (2010). Towards Autonomous Wheelchair Systems in Urban Environments. In: Howard, A., Iagnemma, K., Kelly, A. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13408-1_2
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DOI: https://doi.org/10.1007/978-3-642-13408-1_2
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