Towards Constant-Time Robot Localization in Large Dynamic Environments

Global localization is the problem in which a mobile robot has to estimate the self-position with respect to an a priori map of landmarks using the perception and the motion measurements without using any knowledge of the initial self-position. Recently, random sample consensus (RANSAC), a robust multi-hypothesis estimator, has been successfully applied to offline global localization in static environments. However, online global localization in dynamic environments is still a difficult problem, due to incrementally arriving measurements as well as many outlier measurements. To realize a real time algorithm for such an online process, we have developed an incremental version of RANSAC algorithm by extending an efficient preemptive RANSAC scheme, in order to find inlier hypotheses of self-positions out of a large number of outlier hypotheses contaminated by the outlier measurements

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

[2]  David Nistér,et al.  Preemptive RANSAC for live structure and motion estimation , 2005, Machine Vision and Applications.

[3]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

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

[5]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..

[6]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[7]  Gaurav S. Sukhatme,et al.  Online simultaneous localization and mapping in dynamic environments , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

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

[9]  Lina María Paz,et al.  Global localization in SLAM in bilinear time , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[11]  José A. Castellanos,et al.  Linear time vehicle relocation in SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Bruce A. MacDonald,et al.  Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison , 2005, IEEE Transactions on Robotics.