Scalable Cooperative Localization with Minimal Sensor Configuration

Localization of distributed robots can be improved by fusing the sensor data from each robot collectively in the network. This may allow for each individual robot’s sensor configuration to be reduced while maintaining an acceptable level of uncertainty. However, the scalability of a reduced sensor configuration should be carefully considered lest the propagated error become unbounded in large networks of robots. In this paper, we propose a minimal but scalable sensor configuration for a fleet of vehicles localizing on the urban road. The cooperative localization is proven to be scalable if the sensors’ data are informative enough. The experimental results justify that pose uncertainty will remain at an acceptable level when the number of robots increases.

[1]  Wolfram Burgard,et al.  A Probabilistic Approach to Collaborative Multi-Robot Localization , 2000, Auton. Robots.

[2]  Wolfram Burgard,et al.  Collaborative Multi-Robot Localization , 1999, DAGM-Symposium.

[3]  Gregory Dudek,et al.  Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[5]  Marcelo H. Ang,et al.  Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  M. Jhun,et al.  Asymptotics for the minimum covariance determinant estimator , 1993 .

[7]  Samuel S. Blackman,et al.  Design and Analysis of Modern Tracking Systems , 1999 .

[8]  Fawzi Nashashibi,et al.  Multi-vehicle cooperative perception and augmented reality for driver assistance: A possibility to ‘see’ through front vehicle , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Prabir Barooah,et al.  Distributed collaborative localization of multiple vehicles from relative pose measurements , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[10]  Lynne E. Parker,et al.  Distributed heterogeneous outdoor multi-robot localization , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[11]  Sebastian Thrun,et al.  Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Franck Petit,et al.  On the solvability of the localization problem in robot networks , 2008, 2008 IEEE International Conference on Robotics and Automation.

[13]  Hugh Durrant-Whyte,et al.  Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach , 1995 .

[14]  Gerd Wanielik,et al.  Comparison and evaluation of advanced motion models for vehicle tracking , 2008, 2008 11th International Conference on Information Fusion.

[15]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[16]  Camillo J. Taylor,et al.  A bounded uncertainty approach to multi-robot localization , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[17]  Camillo J. Taylor,et al.  A bounded uncertainty approach to cooperative localization using relative bearing constraints , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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