Now showing items 1-20 of 211

    • The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue 

      Dellaert, Frank; Balch, Tucker; Kaess, Michael; Ravichandran, Ram; Alegre, Fernando; Berhault, Marc; McGuire, Robert; Merrill, Ernest; Moshkina, Lilia; Walker, Daniel (Georgia Institute of TechnologyAAAI Press, 2002)
      We describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup ...
    • The Expectation Maximization Algorithm 

      Dellaert, Frank (Georgia Institute of Technology, 2002)
      This note represents my attempt at explaining the EM algorithm. This is just a slight variation on Tom Minka's tutorial, perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition.
    • An MCMC-based Particle Filter for Tracking Multiple Interacting Targets 

      Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of Technology, 2003)
      We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause ...
    • EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence 

      Dellaert, Frank; Seitz, Steven M.; Thorpe, Charles E.; Thrun, Sebastian (Georgia Institute of TechnologyKluwer Academic Publishers, 2003)
      Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning ...
    • Marco Polo Localization 

      Martinson, Eric Beowulf; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2003-09)
      We introduce the Marco Polo Localization approach, where we apply sound as a tool for gathering range measurements between robots, and use those to solve a range-only Simultaneous Localization and Mapping problem. Range ...
    • Intrinsic Localization and Mapping with 2 Applications: Diffusion Mapping and Marco Polo Localization 

      Dellaert, Frank; Alegre, Fernando; Martinson, Eric Beowulf (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2003-09)
      We investigate Intrinsic Localization and Mapping (ILM) for teams of mobile robots, a multi-robot variant of SLAM where the robots themselves are used as landmarks. We develop what is essentially a straightforward ...
    • Spectral Partitioning for Structure from Motion 

      Steedly, Drew; Essa, Irfan; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2003-10)
      We propose a spectral partitioning approach for large-scale optimization problems, specifically structure from motion. In structure from motion, partitioning methods reduce the problem into smaller and better conditioned ...
    • Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model 

      Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2003-10)
      We describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built ...
    • Reconstruction of Objects with Jagged Edges through Rao-Blackwellized Fitting of Piecewise Smooth Subdivision Curves 

      Kaess, Michael; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2003-10)
      In some applications objects are known to have non-smooth or “jagged” edges, which are not well approximated by smooth curves. We use subdivision curves as a simple but flexible curve representation, which allows tagging ...
    • A Sample of Monte Carlo Methods in Robotics and Vision 

      Dellaert, Frank (Georgia Institute of TechnologyThe Institute of Statistical Mathematics (ISM), 2003-12)
      Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number ...
    • Robust Generative Subspace Modeling: The Subspace t Distribution 

      Khan, Zia; Dellaert, Frank (Georgia Institute of Technology, 2004)
      Linear latent variable models such as statistical factor analysis (SFA) and probabilistic principal component analysis (PPCA) assume that the data are distributed according to a multivariate Gaussian. A drawback of this ...
    • Grammatical Methods in Computer Vision: An Overview 

      Chanda, Gaurav; Dellaert, Frank (Georgia Institute of Technology, 2004)
      We review various methods and applications that have used grammars for solving inference problems in computer vision and pattern recognition. Grammars have been useful because they are intuitively simple to understand, and ...
    • Dirichlet Process based Bayesian Partition Models for Robot Topological Mapping 

      Ranganathan, Ananth; Dellaert, Frank (Georgia Institute of Technology, 2004)
      Robotic mapping involves finding a solution to the correspondence problem. A general purpose solution to this problem is as yet unavailable due to the combinatorial nature of the state space. We present a framework for ...
    • A Probabilistic Approach to the Semantic Interpretation of Building Facades 

      Alegre, Fernando; Dellaert, Frank (Georgia Institute of TechnologyCIPA, 2004)
      Semantically-enhanced 3D model reconstruction in urban environments is useful in a variety of applications, such as extracting metric and semantic information about buildings, visualizing the data in a way that outlines ...
    • Semantic SLAM for Collaborative Cognitive Workspaces 

      Dellaert, Frank; Bruemmer, David (Georgia Institute of Technology, 2004)
    • MCMC-based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points 

      Kaess, Michael; Zboinski, Rafal; Dellaert, Frank (Georgia Institute of TechnologySpringer Berlin / Heidelberg, 2004-05)
      We investigate the automated reconstruction of piecewise smooth 3D curves, using subdivision curves as a simple but flexible curve representation. This representation allows tagging corners to model nonsmooth features ...
    • An MCMC-based Particle Filter for Tracking Multiple Interacting Targets 

      Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of TechnologySpringer Berlin / Heidelberg, 2004-05)
      We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause ...
    • A Rao-Blackwellized Particle Filter for EigenTracking 

      Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2004-06)
      Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black’s influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimi ...
    • Atlanta World: An Expectation Maximization Framework for Simultaneous Low-level Edge Grouping and Camera Calibration in Complex Man-made Environments 

      Schindler, Grant; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2004-06)
      Edges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene understanding and camera calibration. A ...
    • Map-based Priors for Localization 

      Oh, Sang Min; Tariq, Sarah; Walker, Bruce N.; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2004-09)
      Localization from sensor measurements is a fundamental task for navigation. Particle filters are among the most promising candidates to provide a robust and realtime solution to the localization problem. They instantiate the ...