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Dellaert, Frank

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Start date: 2000 × End date: 2009 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Mobile Robot Laboratory ×

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Now showing 1 - 5 of 5
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    Intrinsic Localization and Mapping With 2 Applications: Diffusion Mapping and Marco Polo Localization
    (Georgia Institute of Technology, 2003) Alegre, Fernando ; Dellaert, Frank ; Martinson, Eric Beowulf
    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 application of Bayesian estimation to the problem, and present two complimentary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in practice. We also provide a discussion of the degrees of freedom and ambiguities in the solution. Finally, we introduce two applications of ILM that bring out its potential: Diffusion Mapping and Marco Polo localization.
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    Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model
    (Georgia Institute of Technology, 2003) Balch, Tucker ; Dellaert, Frank ; Khan, Zia
    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 on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.
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    Marco Polo Localization
    (Georgia Institute of Technology, 2003) Dellaert, Frank ; Martinson, Eric Beowulf
    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 is calculated by correlating two recordings of the same sound, recorded on a pair of robots, after which the resulting time delay estimate is converted to a range measurement. The algorithmic approach we use is a straightforward application of the Bayesian estimation framework. We also present two complementary views on the associated optimization problem that provide insight into the problem and allows one to devise initialization strategies, indispensable in a range-only scenario. We illustrate the approach with both simulated and experimental results.
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    Linear 2D Localization and Mapping for Single and Multiple Robot Scenarios
    (Georgia Institute of Technology, 2002) Dellaert, Frank ; Stroupe, Ashley W.
    We show how to recover 2D structure and motion linearly in order to initialize Simultaneous Mapping and Localization (SLAM) for bearings-only measurements and planar motion. The method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Hence, it substantially enlarges the scope in which non-linear batch-type SLAM algorithms can be applied. The method is applicable when at least seven landmarks are seen from three different vantage points, whether by one robot that moves over time or by multiple robots that observe a set of common landmarks.
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    The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue
    (Georgia Institute of Technology, 2002) Alegre, Fernando ; Balch, Tucker ; Berhault, Marc ; Dellaert, Frank ; Kaess, Michael ; McGuire, Robert ; Merrill, Ernest ; Moshkina, Lilia ; Ravichandran, Ram ; Walker, Daniel
    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 exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.