Global localization with detection of changes in non-stationary environments

In this paper, we propose a method for global localization in non-stationary environments, where the environment is partially or completely different from the map. We assume there is no moving object. It is difficult to detect changes when both of the self-position and the map have large uncertainties. To solve the problem, we extended Monte Carlo Localization (MCL) so as to generate a number of hypotheses about the change as well as the self-position. We also introduced Sensor Resetting Localization (SRL), in order to generate initial estimation of self-position, or to recover from large positioning errors. The proposed method has been tested in a number of environments as well as changes. As a results, we found the proposed method is effective even when "Rate Of Changes (ROC)" is high in the environment.

[1]  Ingemar J. Cox,et al.  On Finding Ranked Assignments With Application to Multi-Target Tracking and Motion Correspondence , 1995 .

[2]  Charles C. Nguyen,et al.  Globally Optimal Trajectory Planning for Redundant Manipulators using State Space Augmentation Method , 1997, J. Intell. Robotic Syst..

[3]  Manuela M. Veloso,et al.  Sensor resetting localization for poorly modelled mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Horst-Michael Groß,et al.  Vision-based Monte Carlo self-localization for a mobile service robot acting as shopping assistant in a home store , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Hongbin Zha,et al.  Mobile robot localization with an incomplete map in non-stationary environments , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[6]  Fabio M. Marchese,et al.  A robot localization method based on evidence accumulation and multi-resolution , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Ingemar J. Cox,et al.  Underwater sonar data fusion using an efficient multiple hypothesis algorithm , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

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

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

[10]  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).

[11]  Hugh Durrant-Whyte,et al.  SLAM Updates Require Constant Time , 2002 .

[12]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[13]  Hongbin Zha,et al.  Detecting changes in a dynamic environment for updating its maps by using a mobile robot , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[14]  Rong Chen,et al.  A Theoretical Framework for Sequential Importance Sampling with Resampling , 2001, Sequential Monte Carlo Methods in Practice.

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