A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League

This paper deals with the problem of humanoid robot localization and proposes a new method for position estimation that has been developed for the RoboCup Standard Platform League environment. Firstly, a complete vision system has been implemented in the Nao robot platform that enables the detection of relevant field markers. The detection of field markers provides some estimation of distances for the current robot position. To reduce errors in these distance measurements, extrinsic and intrinsic camera calibration procedures have been developed and described. To validate the localization algorithm, experiments covering many of the typical situations that arise during RoboCup games have been developed: ranging from degradation in position estimation to total loss of position (due to falls, ‘kidnapped robot’, or penalization). The self-localization method developed is based on the classical particle filter algorithm. The main contribution of this work is a new particle selection strategy. Our approach reduces the CPU computing time required for each iteration and so eases the limited resource availability problem that is common in robot platforms such as Nao. The experimental results show the quality of the new algorithm in terms of localization and CPU time consumption.

[1]  C. Fraser,et al.  Digital camera calibration methods: Considerations and comparisons , 2006 .

[2]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[3]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[4]  Thijs Jeffry de Haas,et al.  Efficient and Reliable Sensor Models for Humanoid Soccer Robot Self-Localization , 2009 .

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

[6]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Manuela M. Veloso,et al.  Multi-observation sensor resetting localization with ambiguous landmarks , 2013, Auton. Robots.

[8]  Thomas Röfer,et al.  Particle Filter-based State Estimation in a Competitive and Uncertain Environment , 2007 .

[9]  Oliver Urbann,et al.  Efficient Multi-hypotheses Unscented Kalman Filtering for Robust Localization , 2011, RoboCup.

[10]  Manuel Graña,et al.  Visual Servoing of Legged Robots , 2011, Journal of Mathematical Imaging and Vision.

[11]  M. Sridharan,et al.  Austin Villa 2011 : Sharing is Caring : Better Awareness through Information Sharing , 2012 .

[12]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Felix Wenk,et al.  B-Human 2011 - Eliminating Game Delays , 2012, RoboCup.

[14]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[15]  Julio Vega,et al.  Robot Evolutionary Localization Based on Attentive Visual Short-Term Memory , 2013, Sensors.

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

[17]  Piyush Khandelwal and Matthew Hausknecht and Juhyun Lee a Stone.  Vision Calibration and Processing on a Humanoid Soccer Robot , 2010 .

[18]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Manuel Mazo,et al.  Localization of Mobile Robots Using Odometry and an External Vision Sensor , 2010, Sensors.

[20]  Dah-Jing Jwo,et al.  Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion , 2011, Sensors.

[21]  A. Bais,et al.  Comparison of Self-Localization Methods for Soccer Robots , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[22]  Antoni Burguera,et al.  Sonar Sensor Models and Their Application to Mobile Robot Localization , 2009, Sensors.

[23]  D. Rubin.  Using the SIR algorithm to simulate posterior distributions , 1988 .

[24]  Luis Payá,et al.  Map Building and Monte Carlo Localization Using Global Appearance of Omnidirectional Images , 2010, Sensors.