An Empirical Evaluation of Context-Sensitive Pose Estimators in an Urban Outdoor Environment

Abstract : When a mobile robot is executing a navigational task in an urban outdoor environment, accurate localization information is often essential. The difficulty of this task is compounded by sensor drop-out and the presence of non-linear error sources over the span of the mission. We have observed that certain motions of the robot and environmental conditions affect pose sensors in different ways. In this paper, we propose a computational method for localization that systematically integrates and evaluates contextual information that affects the quality of sensors, and utilize the information in order to improve the output of sensor fusion. Our method was evaluated in comparison with conventional probabilistic localization methods (namely, the extended Kalman filter and Monte Carlo localization) in a set of outdoor experiments. The results of the experiment are also reported in this paper.

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

[2]  Luiz Chaimowicz,et al.  Deploying Air-Ground Multi-Robot Teams in Urban Environments , 2005 .

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

[4]  Ronald C. Arkin,et al.  Multiagent Mission Specification and Execution , 1997, Auton. Robots.

[5]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[6]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[7]  Rudy R. Negenborn,et al.  Robot Localization and Kalman Filters , 2003 .

[8]  Thomas James Robert Ford,et al.  NovAtel''s RT-20,-A Real Time Floating Ambiquity Positioning System , 1994 .

[9]  Stergios I. Roumeliotis,et al.  Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[10]  D. Kurth.  Range-Only Robot Localization and SLAM with Radio , 2004 .

[11]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[12]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

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

[14]  P. Fearnhead,et al.  An improved particle filter for non-linear problems , 1999 .

[15]  Johann Borenstein,et al.  Sensor fusion for mobile robot dead-reckoning with a precision-calibrated fiber optic gyroscope , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[16]  Ronald C. Arkin,et al.  Field Results for Tactical Mobile Robot Missions , 2000 .

[17]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[18]  Dieter Fox,et al.  An experimental comparison of localization methods continued , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.