Towards Lazy Data Association in SLAM

We present a lazy data association algorithm for the simultaneous localization and mapping (SLAM) problem. Our approach uses a tree-structured Bayesian representation of map posteriors that makes it possible to revise data association decisions arbitrarily far into the past. We describe a criterion for detecting and repairing poor data association decisions. This technique makes it possible to acquire maps of large-scale environments with many loops, with a minimum of computational overhead for the management of multiple data association hypotheses. A empirical comparison with the popular FastSLAM algorithm shows the advantage of lazy over proactive data association.

[1]  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).

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

[3]  Hugh F. Durrant-Whyte,et al.  A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  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).

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

[6]  Martin J. Wainwright,et al.  Stochastic processes on graphs with cycles: geometric and variational approaches , 2002 .

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

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

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

[10]  Ingemar J. Cox,et al.  A review of statistical data association techniques for motion correspondence , 1993, International Journal of Computer Vision.

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

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

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

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

[15]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1997, J. Intell. Robotic Syst..

[16]  William Whittaker,et al.  A Case Study in Robotic Mapping of Abandoned Mines , 2003, International Symposium on Field and Service Robotics.

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

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

[19]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

[23]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.

[24]  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).

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

[26]  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).

[27]  Vipin Kumar,et al.  Highly Scalable Parallel Algorithms for Sparse Matrix Factorization , 1997, IEEE Trans. Parallel Distributed Syst..