A comparison of data association techniques for Simultaneous Localization and Mapping

The problem of Simultaneous Localization and Mapping (SLAM) has received a great deal of attention within the robotics literature, and the importance of the solutions to this problem has been well documented for successful operation of autonomous agents in a number of environments. Of the numerous solutions that have been developed for solving the SLAM problem many of the most successful approaches continue to either rely on, or stem from, the Extended Kalman Filter method (EKF). However, the new algorithm FastSLAM has attracted attention for many properties not found in EKF based methods. One such property is the ability to deal with unknown data association and its robustness to data association errors. The problem of data association has also received a great deal of attention in the robotics literature in recent years, and various solutions have been proposed. In an effort to both compare the performance of the EKF and FastSLAM under ambiguous data association situations, as well as compare the performance of three different data association methods a comprehensive study of various SLAM filter-data association combinations is performed. This study will consist of pairing the EKF and FastSLAM filtering approaches with the Joint Compatibility, Sequential Compatibility Nearest Neighbor, and Joint Maximum Likelihood data association methods. The comparison will be based on both contrived simulations as well as application to the publicly available Car Park data set. The simulated results will demonstrate a heavy dependence on geometry, particularly landmark separation, for the performance of both filter performance and the data association algorithms used. The real world data set results will demonstrate that the performance of some data association algorithms, when paired with an EKF, can give identical results. At the same time a distinction in mapping performance between those pairings and the EKF paired with Joint Compatibility data association will be shown. These EKF based pairings will be contrasted to the performance obtained for the FastSLAMSequential Nearest Neighbor marriage. Finally, the difficulties in applying the Joint Compatibility and Joint Maximum Likelihood data association methods using FastSLAM 1.0 for this data set will be discussed. 3 U

[1]  Eduardo Mario Nebot,et al.  Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms , 2003, IEEE Trans. Robotics Autom..

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

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

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

[5]  F. Dellaert,et al.  A Rao-Blackwellized particle filter for EigenTracking , 2004, CVPR 2004.

[6]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[7]  Thia Kirubarajan,et al.  Probabilistic data association techniques for target tracking in clutter , 2004, Proceedings of the IEEE.

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

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

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

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

[12]  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).

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

[14]  Sebastian Thrun,et al.  Particle Filters in Robotics , 2002, UAI.

[15]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[16]  Hugh F. Durrant-Whyte,et al.  Data association for mobile robot navigation: a graph theoretic approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

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

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

[19]  Sebastian Thrun,et al.  FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data , 2004 .

[20]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

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

[22]  Sebastian Thrun,et al.  Learning Hierarchical Object Maps of Non-Stationary Environments with Mobile Robots , 2002, UAI.

[23]  Wolfram Burgard,et al.  A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots , 2004, IEEE Transactions on Robotics and Automation.

[24]  Hugh F. Durrant-Whyte,et al.  The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications , 2001, IEEE Trans. Robotics Autom..

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

[26]  John J. Leonard,et al.  Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization , 2001, ISRR.

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

[28]  Hugh F. Durrant-Whyte,et al.  A Bayesian Algorithm for Simultaneous Localisation and Map Building , 2001, ISRR.

[29]  John J. Leonard,et al.  Towards Constant-Time SLAM on an Autonomous Underwater Vehicle Using Synthetic Aperture Sonar , 2003, ISRR.

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

[31]  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).

[32]  P. R. Nachtsheim.  Solving constraint satisfaction problems , 1989 .

[33]  Sebastian Thrun,et al.  Results for outdoor-SLAM using sparse extended information filters , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

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

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

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

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

[38]  N. D. Freitas.  Rao-Blackwellised particle filtering for fault diagnosis , 2002 .

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

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

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

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

[43]  Ronald Parr,et al.  DP-SLAM 2.0 , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

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