Localization Techniques for Synthetic Reality

@article{funiak-ijrr08,
 author = {Funiak, Stanislav and Pillai, Padmanabhan and
 Ashley-Rollman, Michael P. and Campbell, Jason D. and Goldstein,
 Seth Copen},
 title = {Distributed Localization of Modular Robot Ensembles},
 journal = {International Journal of Robotics Research},
 keywords = {Localization, Modular Robotics},
 volume = {28},
 number = {8},
 year = {2009},
 pages = {946-961},
}

@inproceedings{funiak-rss08,
 author = {Funiak, Stanislav and Pillai, Padmanabhan and
 Ashley-Rollman, Michael P. and Campbell, Jason D. and Goldstein,
 Seth Copen},
 title = {Distributed Localization of Modular Robot Ensembles},
 booktitle = {Proceedings of Robotics: Science and Systems},
 year = {2008},
 month = {June},
 abstract = {Internal localization, the problem of estimating
 relative pose for each module (part) of a modular robot is a
 prerequisite for many shape control, locomotion, and actuation
 algorithms. In this paper, we propose a robust hierarchical
 approach that uses normalized cut to identify dense subregions
 with small mutual localization error, then progressively merges
 those subregions to localize the entire ensemble. Our method
 works well in both 2D and 3D, and requires neither exact
 measurements nor rigid inter-module connectors. Most of the
 computations in our method can be effectively distributed. The
 result is a robust algorithm that scales to large,
 non-homogeneous ensembles. We evaluate our algorithm in accurate
 2D and 3D simulations of scenarios with up to 10,000 modules.},
 keywords = {Distributed Systems, Localization, Meld},
 url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-rss2008.pdf},
}

@inproceedings{funiak-iros07,
 author = {Funiak, Stanislav and Pillai, Padmanabhan and Campbell,
 Jason D. and Goldstein, Seth Copen},
 title = {Internal Localization of Modular Robot Ensembles},
 booktitle = {Workshop on Self-Reconfiguring Modular Robotics at the
 IEEE International Conference on Intelligent Robots and Systems
 (IROS) '07},
 venue = {Workshop on Self-Reconfigurable Robots/Systems and
 Applications at IROS},
 year = {2007},
 month = {October},
 abstract = {The determination of the relative position and pose of
 every robot in a modular robotic ensemble is a necessary
 preliminary step for most modular robotic tasks. Localization is
 particularly important when the modules make local noisy
 observations and are not significantly constrained by inter-robot
 latches. In this paper, we propose a robust hierarchical approach
 to the {\em internal localization} problem that uses normalized
 cut to identify subproblems with small localization error. A key
 component of our solution is a simple method to reduce the cost
 of normalized cut computations. The result is a robust algorithm
 that scales to large, non-homogeneous ensembles. We evaluate our
 algorithm in simulation on ensembles of up to 10,000 modules,
 demonstrating substantial improvements over prior work.},
 keywords = {Probabilistic Inference, Sensing, Localization,
 Distributed Algorithms},
 url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-iros07.pdf},
}

@inproceedings{funiak-ipsn06,
 author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
 Rahul and Paskin, Mark},
 title = {Distributed Localization of Networked Cameras},
 booktitle = {Fifth International Conference on Information
 Processing in Sensor Networks (IPSN'06)},
 venue = {International Conference on Information Processing in
 Sensor Networks (IPSN'06)},
 month = {April},
 pages = {34--42},
 year = {2006},
 keywords = {Probabilistic Inference, Sensing, Distributed
 Algorithms, Graphical Models, Localization},
 url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
 abstract = {Camera networks are perhaps the most common type of
 sensor network and are deployed in a variety of real-world
 applications including surveillance, intelligent environments and
 scientific remote monitoring. A key problem in deploying a
 network of cameras is calibration, i.e., determining the location
 and orientation of each sensor so that observations in an image
 can be mapped to locations in the real world. This paper proposes
 a fully distributed approach for camera network calibration. The
 cameras collaborate to track an object that moves through the
 environment and reason probabilistically about which camera poses
 are consistent with the observed images. This reasoning employs
 sophisticated techniques for handling the difficult
 nonlinearities imposed by projective transformations, as well as
 the dense correlations that arise between distant cameras. Our
 method requires minimal overlap of the cameras' fields of view
 and makes very few assumptions about the motion of the object. In
 contrast to existing approaches, which are centralized, our
 distributed algorithm scales easily to very large camera
 networks. We evaluate the system on a real camera network with 25
 nodes as well as simulated camera networks of up to 50 cameras
 and demonstrate that our approach performs well even when
 communication is lossy.},
}

@mastersthesis{reshko04,
 author = {Reshko, Greg},
 venue = {Masters Thesis},
 title = {Localization Techniques for Synthetic Reality},
 school = {Carnegie Mellon University},
 keywords = {Localization},
 year = {2004},
}