Online probabilistic topological mapping

We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao—Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.

[1]  Frank Dellaert,et al.  Bayesian surprise and landmark detection , 2009, 2009 IEEE International Conference on Robotics and Automation.

[2]  Michael Beetz,et al.  RG Mapping : Building Object-Oriented Representations of Structured Human Environments , .

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

[4]  Benjamin Kuipers,et al.  Towards a general theory of topological maps , 2004, Artif. Intell..

[5]  James D. Weiland,et al.  Visual loop closing using multi-resolution SIFT grids in metric-topological SLAM , 2009, CVPR.

[6]  H. Durrant-Whyte,et al.  Simultaneous Localisation and Mapping ( SLAM ) : Part II State of the Art , 2006 .

[7]  Benjamin Kuipers,et al.  Loop-closing and planarity in topological map-building , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[8]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[9]  Paul Newman,et al.  Outdoor SLAM using visual appearance and laser ranging , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[10]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[11]  Wolfram Burgard,et al.  Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach , 1998, AAAI/IAAI.

[12]  Benjamin Kuipers,et al.  Using the topological skeleton for scalable global metrical map-building , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[13]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[14]  Frank Dellaert,et al.  A Rao-Blackwellized particle filter for topological mapping , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  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).

[16]  Ronald Parr,et al.  DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks , 2003, IJCAI.

[17]  David Kauchak,et al.  Modeling word burstiness using the Dirichlet distribution , 2005, ICML.

[18]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[19]  Albert Nijenhuis,et al.  Combinatorial Algorithms for Computers and Calculators , 1978 .

[20]  Frank Dellaert,et al.  Inference in the space of topological maps: an MCMC-based approach , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[21]  Michael Bosse,et al.  Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework , 2004, Int. J. Robotics Res..

[22]  Tom Duckett,et al.  Incremental Topological Mapping Using Omnidirectional Vision , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Gregory Dudek,et al.  Robust place recognition using local appearance based methods , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[24]  P. Fearnhead,et al.  On‐line inference for hidden Markov models via particle filters , 2003 .

[25]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[26]  Frank Dellaert,et al.  A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[27]  Wolfram Burgard,et al.  Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM , 1999, ICML.

[28]  Ricardo Gutierrez-Osuna,et al.  LOLA Probabilistic Navigation for Topological Maps , 1996, AI Mag..

[29]  Benjamin Kuipers,et al.  Bootstrap learning for place recognition , 2002, AAAI/IAAI.

[30]  Javier González,et al.  Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM , 2008, IEEE Transactions on Robotics.

[31]  Benjamin Kuipers,et al.  Local metrical and global topological maps in the hybrid spatial semantic hierarchy , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[32]  Paul Newman,et al.  FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance , 2008, Int. J. Robotics Res..

[33]  T. Minka.  Estimating a Dirichlet distribution , 2012 .

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

[35]  Maja J. Matarić,et al.  A Distributed Model for Mobile Robot Environment-Learning and Navigation , 1990 .

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

[37]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[38]  Frank Dellaert,et al.  EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence , 2004, Machine Learning.

[39]  Benjamin Kuipers,et al.  A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations , 1991, Robotics Auton. Syst..

[40]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[41]  D. Blackwell,et al.  Ferguson Distributions Via Polya Urn Schemes , 1973 .

[42]  Jean-François Mari,et al.  Place learning and recognition using hidden Markov models , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[43]  Wolfram Burgard,et al.  Towards Lazy Data Association in SLAM , 2003, ISRR.

[44]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[45]  Roland Siegwart,et al.  Hybrid simultaneous localization and map building: closing the loop with multi-hypotheses tracking , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[46]  Emanuele Menegatti,et al.  Bayesian inference in the space of topological maps , 2006, IEEE Transactions on Robotics.

[47]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[48]  Gaurav S. Sukhatme,et al.  Incremental online topological map building with a mobile robot , 1999, Optics East.

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

[50]  Luc Van Gool,et al.  Omnidirectional Vision Based Topological Navigation , 2007, International Journal of Computer Vision.

[51]  Gregory Dudek,et al.  Using Local Information in a Non-Local Way for Mapping Graph-Like Worlds , 1993, IJCAI.

[52]  Pat Langley,et al.  Place recognition in dynamic environments , 1997, J. Field Robotics.

[53]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[54]  James J. Little,et al.  Vision-based global localization and mapping for mobile robots , 2005, IEEE Transactions on Robotics.

[55]  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.

[56]  Leslie Pack Kaelbling,et al.  Learning Topological Maps with Weak Local Odometric Information , 1997, IJCAI.

[57]  Robert C. Bolles,et al.  A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data , 1981, IJCAI.

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

[59]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[60]  Jean-Arcady Meyer,et al.  Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.

[61]  Benjamin Kuipers,et al.  Map Learning with Uninterpreted Sensors and Effectors , 1995, Artif. Intell..

[62]  Kevin Lynch,et al.  The Image of the City , 1960 .

[63]  Ian D. Reid,et al.  Real-Time SLAM Relocalisation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[64]  Javier González,et al.  Consistent observation grouping for generating metric-topological maps that improves robot localization , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..