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Dellaert, Frank

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End date: 2011 × Start date: 2010 × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Computing × Type: Text ×

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Now showing 1 - 5 of 5
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    Bio-Inspired Navigation
    (Georgia Institute of Technology, 2011-12) Dellaert, Frank ; Gill, Tarandeep
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    Bundle Adjustment in Large-Scale 3D Reconstructions based on Underwater Robotic Surveys
    (Georgia Institute of Technology, 2011-06) Beall, Chris ; Dellaert, Frank ; Mahon, Ian ; Williams, Stefan B.
    In this paper we present a technique to generate highly accurate reconstructions of underwater structures by employing bundle adjustment on visual features, rather than relying on a filtering approach using navigational sensor data alone. This system improves upon previous work where an extended information filter was used to estimate the vehicle trajectory. This filtering technique, while very efficient, suffers from the shortcoming that linearization errors are irreversibly incorporated into the vehicle trajectory estimate. This drawback is overcome by applying smoothing and mapping to the full problem. In contrast to the filtering approach, smoothing and mapping techniques solve for the entire vehicle trajectory and landmark positions at once by performing bundle adjustment on all the visual measurements taken at each frame. We formulate a large nonlinear least-squares problem where we minimize the pixel projection error of each of the landmark measurements. The technique is demonstrated on a large-scale underwater dataset, and it is also shown that superior results are achieved with smoothing and mapping as compared to the filtering approach.
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    Online Probabilistic Topological Mapping
    (Georgia Institute of Technology, 2011-01-24) Ranganathan, Ananth ; Dellaert, Frank
    We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao–Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.
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    Learning Visibility of Landmarks for Vision-Based Localization
    (Georgia Institute of Technology, 2010) Alcantarilla, Pablo F. ; Oh, Sang Min ; Mariottini, Gian Luca ; Bergasa, Luis M. ; Dellaert, Frank
    We aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from the image with the map elements at the current location. Although the problem of data association has greatly benefited from recent advances in appearance-based matching methods, less attention has been paid to the effective use of the geometric relations between the 3D map and the camera in the matching process. In this paper we propose to exploit the geometric relationship between the 3D map and the camera pose to determine the visibility of the features. In our approach, we model the visibility of every map feature w.r.t. the camera pose using a non-parametric distribution model. We learn these non-parametric distributions during the 3D reconstruction process, and develop efficient algorithms to predict the visibility of features during localization. With this approach, the matching process only uses those map features with the highest visibility score, yielding a much faster algorithm and superior localization results. We demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the localization in large and cluttered environments.
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    DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs
    (Georgia Institute of Technology, 2010) Cunningham, Alexander ; Paluri, Manohar ; Dellaert, Frank
    We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.