Robust Monte Carlo localization for mobile robots

[1]  Michael O. Kolawole,et al.  Estimation and tracking , 2002 .

[2]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

[3]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[4]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[6]  Stergios I. Roumeliotis,et al.  Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  Wolfram Burgard,et al.  Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva , 2000, Int. J. Robotics Res..

[8]  Wolfram Burgard,et al.  Monte Carlo Localization with Mixture Proposal Distribution , 2000, AAAI/IAAI.

[9]  Manuela M. Veloso,et al.  Sensor resetting localization for poorly modelled mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[10]  Johannes Reuter.  Mobile robot self-localization using PDAB , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2000, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Wolfram Burgard,et al.  Experiences with an Interactive Museum Tour-Guide Robot , 1999, Artif. Intell..

[13]  Wolfram Burgard,et al.  Collaborative Multi-Robot Localization , 1999, DAGM-Symposium.

[14]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[15]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[16]  Wolfram Burgard,et al.  Using the CONDENSATION algorithm for robust, vision-based mobile robot localization , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[17]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[18]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[19]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[20]  Sridhar Mahadevan,et al.  Robust Mobile Robot Navigation using Partially-Observable Semi-Markov Decision Processes , 1999 .

[21]  S. Thrun,et al.  Mosaicing a Large Number of Widely Dispersed, Noisy, and Distorted Images: A Bayesian Approach , 1999 .

[22]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[23]  Wolfram Burgard,et al.  Active Markov localization for mobile robots , 1998, Robotics Auton. Syst..

[24]  Wolfram Burgard,et al.  An experimental comparison of localization methods , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[25]  Wolfram Burgard,et al.  Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[26]  Daphne Koller,et al.  Using Learning for Approximation in Stochastic Processes , 1998, ICML.

[27]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[28]  Hugh Durrant-Whyte,et al.  Towards Autonomous Excavation , 1998 .

[29]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[30]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[31]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[32]  Peter I. Corke,et al.  Experiments in autonomous underground guidance , 1997, Proceedings of International Conference on Robotics and Automation.

[33]  Geoffrey E. Hinton,et al.  A Mobile Robot That Learns Its Place , 1997, Neural Computation.

[34]  Karen Zita Haigh,et al.  A layered architecture for office delivery robots , 1997, AGENTS '97.

[35]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[36]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[37]  Joachim Hertzberg,et al.  Landmark-based autonomous navigation in sewerage pipes , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[38]  J.-S. Gutmann,et al.  AMOS: comparison of scan matching approaches for self-localization in indoor environments , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[39]  Randall D. Beer,et al.  Spatial learning for navigation in dynamic environments , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[40]  Leslie Pack Kaelbling,et al.  Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[41]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[42]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[43]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[44]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[45]  Reid G. Simmons,et al.  Passive Distance Learning for Robot Navigation , 1996, ICML.

[46]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[47]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[48]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..

[49]  Swaminathan Natarajan.  Imprecise and Approximate Computation , 1995 .

[50]  Shlomo Zilberstein,et al.  Approximate Reasoning Using Anytime Algorithms , 1995 .

[51]  Ewald von Puttkamer,et al.  Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[52]  Ingemar J. Cox,et al.  Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach , 1994, Artif. Intell..

[53]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[54]  Roman E. Maeder.  Ray Tracing and Graphics Extensions , 1994 .

[55]  S. Ito,et al.  Navigation system based on ceiling landmark recognition for autonomous mobile robot , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[56]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[57]  Wolfgang D. Rencken,et al.  Concurrent localisation and map building for mobile robots using ultrasonic sensors , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[58]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1992 .

[59]  Drew McDermott,et al.  Error correction in mobile robot map learning , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[60]  Alan E. Gelfand,et al.  Bayesian statistics without tears: A sampling-resampling perspective , 1992 .

[61]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[62]  Eric P. Fox.  Bayesian Statistics 3 , 1991 .

[63]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[64]  Ingemar J. Cox,et al.  Blanche-an experiment in guidance and navigation of an autonomous robot vehicle , 1991, IEEE Trans. Robotics Autom..

[65]  Andrew L. Rukhin,et al.  Tools for statistical inference , 1991 .

[66]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[67]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[68]  Andrew W. Moore,et al.  Efficient memory-based learning for robot control , 1990 .

[69]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[70]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[71]  R. Hinkel,et al.  ENVIRONMENT PERCEPTION WITH A LASER RADAR IN A FAST MOVING ROBOT , 1988 .

[72]  Miomir Vukobratović.  Introduction to Robotics , 1988 .

[73]  Mark S. Boddy,et al.  An Analysis of Time-Dependent Planning , 1988, AAAI.

[74]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[75]  D. Rubin.  Using the SIR algorithm to simulate posterior distributions , 1988 .

[76]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[77]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[78]  Jon Louis Bentley,et al.  Multidimensional divide-and-conquer , 1980, CACM.

[79]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[80]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[81]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[82]  C. Stein.  Inadmissibility of the Usual Estimator for the Mean of a Multivariate Normal Distribution , 1956 .