A Discussion of Simultaneous Localization and Mapping

Brooks, R. 1985, ‘Visual map making for a mobile robot'. In: Proceedings of the IEEE International Conference on Robotics and Automation, St. Louis. pp. 824–829.

Castellanos, J., Montiel, J., Neira, J., and Tardos, J. 1999. The SPmap: A probablistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 15(5):948–952.

Chatila, R. and Laumond J. 1985. Position referencing and consistent world modeling for mobile robots. In: Proc. of the IEEE International Conference on Robotics and Automation, St. Louis, pp. 138–145.

Cheeseman, R. and Smith P. 1986. On the representation and estimation of spatial uncertainty. International Journal of Robotics 5:56–68.

Cox, I.J. and Wilfong G.T. (eds.), 1990. In Autonomous Robot Vehicles, New York. Springer Verlag:

Duckett, T. 2003. A genetic algorithm for simultaneouslocalization and mapping. In: Proc. of the IEEEInternational Conference on Robotics and Automation (ICRA'2003), Taipei, pp. 434–439.

Duckett, T., Marsland S., and Shapiro, J. 2002. Fast, on-line learning of globally consistent maps. In Autonomous Robots 12(3):287–300.

Durrant-Whyte, H. 1988. Uncertain geometry in robotics. IEEE Transactions on Robotics and Automation, 4(1):23–31.

Durrant-Whyte, H., Rye D., and Nebot, E. 1995, Localization of Autonomous Guided Vehicles. In: G. Hirzinger and G. Giralt (eds.), In: Proc. of the 8th International Symposium on Robotics Research. pp. 613 –625, SpringerVerlag: New York.

Eliazar, A. and Parr, R. 2003. DP-SLAM: Fast, Robust Simulataneous Localization and Mapping without Predetermined Landmarks. In: Proc. of the 18th International Joint Conference on Artificial Intelligence, Acapulco, pp. 1135–1142.

Eustice, R., Singh, H., and Leonard, J. 2005. Exactly sparse delayed state filters. In: Proc. of the Internation Conference on Robotics and Automation, Barcelona. pp. 2428–2435.

Faugeras, O. 1989. Maintaining representations of the environment of a mobile robot. IEEE Transactions on Robotics and Automation, 5(6):804–819.

Frese, U. 2004. An O(log n) Algorithm for simulateneous localization and mapping of mobile robots in indoor environments. Ph.D. thesis, University of Erlangen-Nürnberg.

Frese, U. 2005a. Animation illustrating SLAM uncertainty structure. http://www.informatik.uni-bremen.de/ ufrese/slamdiscussion_e.html.

Frese, U. 2005b. A proof for the approximate sparsity of SLAM information matrices. In: Proc. of the IEEE International Conference on Robotics and Automation, Barcelona, pp. 331–337.

Frese, U. 2005c. Treemap: An O(log n) Algorithm for simultaneous localization and mapping. Autonomus Robots (submitted).

Frese, U. and Hirzinger, G. 2001. Simultaneous localization and mapping---a discussion. In: Proc. of the IJCAI Workshop on Reasoning with Uncertainty in Robotics, Seattle, pp. 17–26.

Frese, U., Larsson, P., and Duckett, T. 2004. A multigrid algorithm for simultaneous localization and mapping. IEEE Transactions on Robotics 21(2):1–12.

Gauss, C. 1821. Theoria combinationis observationum erroribus minimis obnoxiae. Commentationes Societatis Regiae Scientiarum Gottingensis Recentiores, 5:6–93.

Gelb, A. (Ed.). 1974. Applied Optimal Estimation. MIT Press: Cambridge.

Grisetti, G., Stachniss, C., and Burgard, W. 2005. Improving grid-based SLAM with rao-blackwellized particle filters by adaptive proposals and selectiveresampling. In: Proc. of the IEEE International Conference on Robotics and Automation, Barcelona, pp. 667–672.

Guivant, J. and Nebot, E. 2001. Optimization of the simultaneouslocalization and map-building algorithm for real-timeimplementation. IEEE Transactions on Robotics and Automation, 17(3):242–257.

Guivant, J. and Nebot, E. 2003. Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms. IEEE Transactions on Robotics and Automation, 19(4):749–755.

Gutmann, J. and Konolige, K. 1999. Incremental mapping of large cyclic environments. In: Proc. of the IEEE International Symposium onComputational Intelligence in Robotics and Automation, Monterey, pp. 318–325.

Hähnel, D., Burgard, W., Fox, D., and Thrun, S. 2003. An efficient FastSLAM algorithm for generating maps of large-scale cyclic environmentsfrom raw laser range measurements. In: Proc. of theInternational Conference on Intelligent Robots and Systems, Las Vegas, pp. 206–211.

Hähnel, D., Burgard, W., Wegbreit, B., and Thrun, S. 2003. Towards lazy data association in SLAM'. In: Proc. of the 10th International Symposium of Robotics Research (ISRR'03).

Hebert, P., Betge-Brezetz, S., and Chatila, R. 1995. Probabilistic map learning, necessity and difficulty. In: Proc. of theInternational Workshop Reasoning with Uncertainty in Robotics. pp. 307–320.

Julier, S.J. and Uhlmann, J.K. 2001. A counter example to the theory of simultaneous localization and map building. In Proceedings of the IEEE International Conference on Robotics and Automation, Seoul. pp. 4238–4243.

Kalman, R. 1960. A New approach to linear filtering and prediction problems. Transaction of the ASME Journal of Basic Engineering, 35–45.

Kelly, A. 2000. Some useful results for closed-form propagation of error in vehicle odometry. Technical Report CMU-RI-TR-00-20, Carnegie Mellon University.

Leonard, J. and Durrant-Whyte, H. 1992. Dynamic map building for an autonomous mobile robot. The International Journal on Robotics Research, 11(4):286–298.

Lu, F. and Milios, E. 1997. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4:333–349.

Montemerlo, M. and Thrun, S. 2003. Simultaneous localization and mapping with unknown data association using fastSLAM. In: Proc. of the International Conference on Robotics and Automation, Taipei, pp. 1985–1991.

Montemerlo, M., Thrun, S., Koller, D., and Wegbreit, B. 2002, FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: Proc. of the AAAI National Conference on Artificial Intelligence, Edmonton, pp. 593–598.

Moravec, H. and Elfes, A. 1985. High resolution maps from wide angle sonar. In: Proc. of the IEEE International Conference Robotics and Automation, St. Louis, pp. 116–121.

Murphy, K. 1999. Bayesian map learning in dynamic environments. In Advances in Neural Information Processing Systems (NIPS), Denver, Vol. 12, pp. 1015–1021.

Neal, R. 1993. Probabilistic inference using markov chain monte carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto.

Neira, J. and Tardos, J. 2000. Robust and feasible data asssociation for simultaneous localization and map building. In ICRA Workshop SLAM, San Francisco.

Newman, P. 1999. On the structure and solution of the simultaneous localisation and map building problem. Ph.D. thesis, Deptartment of Mechanical and Mechatronic Engineering, Sydney.

Nieto, J., Guivant, J., Nebot, E., and Thrun, S. 2003. Real time data association for fastSLAM'. In: Proc. of the IEEE Conference on Robotics and Autonomation, Taipeh, pp. 412–418.

Paskin, M. 2003. Thin junction tree filters for simultaneous localization and mapping. In Proceedings of the 18th International Joint Conference on Artificial Intelligence. San Francisco, pp. 1157–1164.

Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. 1992, Numerical Recipes, Second Edition, Cambridge University Press: Cambridge.

Ranganathan, A. and Dellaert, F. 2004. Inference In the space of topological maps: An MCMC-based approach. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, pp. 2518–2523.

Smith, R., Self, M., and Cheeseman, P. 1988. Estimating Uncertain Spatial Relationships in Robotics. In I. Cox and G. Wilfong (eds.). Autonomous Robot Vehicles, New York, Springer Verlag: pp. 167–193.

Tardos, J. 1992. Representing partial and uncertain sensorial information using the theory of symmetries. In: Proc. of the IEEE International Conference on Robotics and Automation, Nice, pp. 1799–1804.

Thrun, S. 2002. Robotic mapping: A survey, In: G. Lakemeyer and B. Nebel (eds.), Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, Section 1.

Thrun, S., Burgard, W. and Fox, D. 1998. A Probabilistic Approach to concurrent mapping and localization for mobile robot. Machine Learning, 31(5):1–25.

Thrun, S., Burgard, W., and Fox, D. 2005. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). MIT Press.

Thrun, S., Koller, D., Ghahramani Z., Durrant-Whyte H., and N. A.Y.: 2002. Simultaneous mapping and localization with sparse extended information filters: theory and initial results. In: Proc. of the Fifth International Workshop on Algorithmic Foundations of Robotics, Nice.

Triggs, W., McLauchlan, P., Hartley, R., and Fitzgibbon, A. 2000. Bundle adjustment – A modern synthesis. In W. Triggs, A. Zisserman, and R. Szeliski (eds.). Vision Algorithms: Theory and Practice, LNCS, Springer Verlag, pp. 298–375.

Uhlmann, J., Julier, S., and Csorba, M. 1997. Nondivergent simultaneous map building and localization using covariance intersection. In: Proc. of the SPIE Conference on Navigation and Control Technologies for Unmanned Systems II, Vol. 3087, pp. 2–11.

Walter M., Eustice, R., and Leonard J. 2005. A provably consistent method for imposing exact sparsity in feature-based SLAM information filters. In Proceedings of the 12th International Symposium of Robotics Research.