FastSLAM: a factored solution to the simultaneous localization and mapping problem

The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.

[1]  W. G. Madow.  On the Theory of Systematic Sampling, II , 1944 .

[2]  W. G. Madow.  On the Theory of Systematic Sampling, III. Comparison of Centered and Random Start Systematic Sampling , 1953 .

[3]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

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

[5]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

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

[7]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  Editors , 1986, Brain Research Bulletin.

[10]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[11]  W. Eric L. Grimson,et al.  Localizing Overlapping Parts by Searching the Interpretation Tree , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

[15]  Raja Chatila,et al.  Stochastic multisensory data fusion for mobile robot location and environment modeling , 1989 .

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

[17]  Raja Chatila,et al.  An Experimental System for Incremental Environment Modelling by an Autonomous Mobile Robot , 1989, ISER.

[18]  David B. Lomet,et al.  The hB-tree: a multiattribute indexing method with good guaranteed performance , 1990, TODS.

[19]  J. Aplevich,et al.  Lecture Notes in Control and Information Sciences , 1979 .

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

[21]  C Tomasi,et al.  Shape and motion from image streams: a factorization method. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[23]  David J. Goodman,et al.  Personal Communications , 1994, Mobile Communications.

[24]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

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

[26]  H. J. Moore,et al.  Overview of the Mars Pathfinder mission and assessment of landing site predictions. , 1997, Science.

[27]  Andrew J. Davison,et al.  Mobile Robot Navigation Using Active Vision , 1998 .

[28]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[29]  BurgardWolfram,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998 .

[30]  Robin R. Murphy,et al.  Artificial intelligence and mobile robots: case studies of successful robot systems , 1998 .

[31]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[32]  Andrew W. Moore,et al.  Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees , 1998, NIPS.

[33]  Kevin P. Murphy,et al.  Bayesian Map Learning in Dynamic Environments , 1999, NIPS.

[34]  Hugh F. Durrant-Whyte,et al.  An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building , 1999, ISER.

[35]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[36]  Paul Newman,et al.  On the Structure and Solution of the Simultaneous Localisation and Map Building Problem , 1999 .

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

[38]  Lindsay Kleeman,et al.  Feature-Based Mapping in Real, Large Scale Environments Using an Ultrasonic Array , 1999, Int. J. Robotics Res..

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

[40]  A. Winsor.  Sampling techniques. , 2000, Nursing times.

[41]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[42]  Martial Hebert,et al.  Experimental Comparison of Techniques for Localization and Mapping Using a Bearing-Only Sensor , 2000, ISER.

[43]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[44]  John J. Leonard,et al.  A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization , 2000 .

[45]  Arnaud Doucet,et al.  Sequential Monte Carlo Methods to Train Neural Network Models , 2000, Neural Computation.

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

[47]  Hugh F. Durrant-Whyte,et al.  Field Robots , 2001, ISRR.

[48]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[49]  Jeffrey K. Uhlmann,et al.  Building a million beacon map , 2001, SPIE Optics East.

[50]  S. Thrun,et al.  Monte carlo em for data-association and its applications in computer vision , 2001 .

[51]  Eduardo Mario Nebot,et al.  Optimization of the simultaneous localization and map-building algorithm for real-time implementation , 2001, IEEE Trans. Robotics Autom..

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

[53]  Juan D. Tardós,et al.  Data association in stochastic mapping using the joint compatibility test , 2001, IEEE Trans. Robotics Autom..

[54]  Bin Yu,et al.  Model Selection and the Principle of Minimum Description Length , 2001 .

[55]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[56]  John J. Leonard,et al.  Robust Mapping and Localization in Indoor Environments Using Sonar Data , 2002, Int. J. Robotics Res..

[57]  Michael Bosse,et al.  Mapping Partially Observable Features from Multiple Uncertain Vantage Points , 2002, Int. J. Robotics Res..

[58]  William Whittaker,et al.  Conditional particle filters for simultaneous mobile robot localization and people-tracking , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[59]  Hugh F. Durrant-Whyte,et al.  Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results , 2004, WAFR.

[60]  Hugh F. Durrant-Whyte,et al.  Robust Simultaneous Localization and Mapping for Very Large Outdoor Environments , 2002, ISER.

[61]  Timothy S. Bailey,et al.  Mobile Robot Localisation and Mapping in Extensive Outdoor Environments , 2002 .

[62]  Wolfram Burgard,et al.  Map building with mobile robots in populated environments , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[63]  John J. Leonard,et al.  Explore and return: experimental validation of real-time concurrent mapping and localization , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[64]  Sebastian Thrun,et al.  Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[65]  Sebastian Thrun,et al.  Simultaneous localization and mapping with unknown data association using FastSLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[66]  Jeffrey Scott Vitter,et al.  Bkd-Tree: A Dznamic Scalable kd-Tree , 2003, SSTD.

[67]  Eduardo Mario Nebot,et al.  Real time data association for FastSLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[68]  Wolfram Burgard,et al.  A system for volumetric robotic mapping of abandoned mines , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[69]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[70]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[71]  Sebastian Thrun,et al.  FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges , 2003, IJCAI.

[72]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[73]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[74]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.