The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Occupancy grid mapping algorithms assume that grid block values are independently distributed. However, most environments of interest contain spatial patterns that are better characterized by models that capture dependencies among grid blocks. To account for such dependencies, we model the envi- ronment as a pairwise Markov random field. We specify a belief propagation-based mapping algorithm that takes these depen- dencies into account when estimating a map. To demonstrate the potential benefits of this approach, we simulate a simple multi- robot minefield mapping scenario. Minefields contain spatial dependencies since some landmine configurations are more likely than others, and since clutter, which causes false alarms, can be concentrated in certain regions and completely absent in others. Our belief propagation-based approach outperforms conventional occupancy grid mapping algorithms in the sense that better maps can be obtained with significantly fewer robot measurements. The belief propagation algorithm requires a modest amount of increased computation, but we contend that in applications where significant energy and time expenditure is associated with robot movement and active sensing, the reduction in the required number of samples will justify the increased computation.

Notes

Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics

Associated Project(s): Wide Area Prospecting Using Supervised Autonomous Robots

Number of pages: 6

Text Reference

Yaron Rachlin, John M. Dolan, and Pradeep Khosla, "Efficient Mapping Through Exploitation of Spatial Dependencies," Proceedings of the International Conference on Intelligent Robots and Systems (IROS '05), 2005.

BibTeX Reference

@inproceedings{Rachlin_2005_5420,
   author = "Yaron Rachlin and John M Dolan and Pradeep Khosla",
   title = "Efficient Mapping Through Exploitation of Spatial Dependencies",
   booktitle = "Proceedings of the International Conference on Intelligent Robots and Systems (IROS '05)",
   year = "2005",
}

The Robotics Institute

Menu