Abstract
Map building is one of the core competencies of truly autonomous robots. Numerous techniques have been developed to represent the static and dynamic environments as well as the perceptional sensing frameworks so far. In this paper, on the basis of our previous work, we compare various sensor systems in building the static and dynamic environment map with the segment-based map and Fuzzy-Tuned Grid-Based Map (FTGBM) strategies. From the comparative results of experiments, we propose a probably efficient and trade-off framework which balances the accuracy of the map against the overall system cost.
Similar content being viewed by others
References
Thrun, S.: Robotic mapping: A survey, Technical Report CMU-CS-02-111, CMU, 2002
Garulli, A., Giannitrapani, A., Rossi, A., Vicino, A.: Simultaneous localization and map building using linear features. In: Proceedings of the 2nd European Conference on Mobile Robots, Ancona (Italy), September 7–10, 2005
Ip, Y.L.: Studies on map building and exploration strategies for Autonomous Mobile Robots (AMRs) [PhD dissertation]. Hong Kong: Department of Electrical Engineering, Hong Kong Polytechnic University (2003)
Chow, K.M., Rad, A.B., Ip, Y.L.: Enhancement of probabilistic grid-based map for mobile robot applications. J. Intell. Robot. Syst. 34(2), 155–174 (2002), June
Remolina, E., Kuipers, B.: Towards a general theory of topological maps. Artif. Intell. 152, 47–104 (2004)
Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artif. Intell. 99(1), 21–71 (1998)
Burgard, W., Cremers, A.B., Fox, D., Hähnel, D., Schulz, D., Steiner, W., Thrun, S.: Experiences with an interactive museum tour-guide robot. Artif. Intell. 114(1–2), 3–55 (1999)
Fox, D., Burgard, W., Thrun, S.: Markov localization for mobile robots in dynamic environments. J. Artif. Intell. Res. 11, 391–427 (1999)
Montemerlo, M., Whittaker, W., Thrun, S.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 695–701, 2002
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings. AAAI National Conference on Artificial Intelligence, pp. 593–598, 2002
Biswas, R., Limketkai, B., Sanner, S., Thrun, S.: Towards object mapping in non-stationary environments with mobile robots. In: Proceedings of International Conference on Intelligent Robots and Systems, pp. 1014–1019, 2002
Andrade-Cetto, J., Sanfeliu, A.: Concurrent map building and localization on indoor dynamic environments. Int. J. Pattern Recogn. Artif. Intell. 16, 361–374 (2002)
Hähnel, D., Schulz, D., Burgard, W.: Mobile robot mapping in populated environments. Adv. Robot. 17(7), 579–598 (2003)
Wolf, D., Sukhatme, G.: Mobile robot simultaneous localization and mapping in dynamic environments. Auton. Robots 19, 53–65 (2005)
Wang, C.C.: Simultaneous localization, mapping and moving object tracking [PhD thesis]. USA: Carnegie Mellon University (2004)
Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, New Jersey (1997)
Oriolo, G., Ulivi, G., Vendittelli, M.: Real-time map building and navigation for autonomous robots in unknown environments. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 28(3), 316–333 (1998)
Oriolo, G., Ulivi, G., Vendittelli, M.: Fuzzy maps: a new tool for mobile robot perception and planning. J. Robot. Syst. 14(3), 179–197 (1997)
Huang, G.Q., Rad, A.B., Wong, Y.K.: A new solution to map dynamic indoor environments. Int. J. Adv. Robot. Syst. 3(3), 119–210 (2006)
Lee, S.J., Cho, D.W., Chung, W.K., Lim, J.H., Kang, C.U.: Feature based map building using sparse sonar data. In: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on. pp. 1648–1652, 2005
Takezawa, S., Dissanayake, G.: Simultaneous localisation and mapping problems in indoor environments with stereo vision. In: Industrial Electronics Society, 2005. IECON 2005. 32nd Annual Conference of IEEE. 6–10 Nov. 2005, pp. 1896–1901 (2005)
Jensfelt, P., Folkesson, J., Kragic, D., Christensen, H.: Exploiting distinguishable image features in robotic mapping and localization. In: Christensen, H.I. (ed.) 1st European Robotics Symposium (EUROS-06), Palermo, Italy, (2006), Mar
Gross, H.M., Koenig, A., Mueller, S.: Omniview-based concurrent map building and localization using adaptive appearance maps. In: Systems, Man and Cybernetics, 2005 IEEE International Conference on. 10–12 Oct. 2005. vol. 4, pp. 3510–3515
Se, S., Lowe, D., Little, J.: Vision-based mobile robot localization and mapping using scale-invariant features. In: Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on. vol. 2, pp. 2051–2058, 2001
Li, X.D., Huang, X.H., Wang, M.: Robot map building from sonar and laser information using DSmT with discounting theory. Int. J. Inf. Technol. 3(2) (2006)
Saphira Manual Version 6.1e. Active media Inc.; April 1998
Mak, M.W., Kung, S.Y., Lin, S.H.: Expectation-maximization theory. (Sample Chapter is provided courtesy of Prentice Hall PTR. Jan 3, 2005)
Dellaert, F.: The expectation maximization algorithm. GVU Center; College of Computing; Georgia Tech, GIT-GVU-02-20, 2002
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, X.Z., Rad, A.B., Wong, Y.K. et al. A Comparative Study of Three Mapping Methodologies. J Intell Robot Syst 49, 385–395 (2007). https://doi.org/10.1007/s10846-007-9143-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-007-9143-z