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Dellaert, Frank

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Now showing 1 - 6 of 6
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    Bio-Inspired Navigation
    (Georgia Institute of Technology, 2011-12) Dellaert, Frank ; Gill, Tarandeep
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    Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment
    (Georgia Institute of Technology, 2011-11) Jian, Yong-Dian ; Balcan, Doru C. ; Dellaert, Frank
    We present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either direct or iterative methods as the linear solver, GSP combines their advantages and is significantly faster on large datasets. Similar to [11], the main idea is to identify a sub-problem (subgraph) that can be solved efficiently by sparse factorization methods and use it to build a preconditioner for the conjugate gradient method. The difference is that GSP is more general and leads to much more effective preconditioners. We design a greedy algorithm to build subgraphs which have bounded maximum clique size in the factorization phase, and also result in smaller condition numbers than standard preconditioning techniques. When applying the proposed method to the “bal” datasets [1], GSP displays promising performance.
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    Collaborative Stereo
    (Georgia Institute of Technology, 2011-09) Achtelik, Markus W. ; Weiss, Stephan ; Chli, Margarita ; Dellaert, Frank ; Siegwart, Roland
    In this paper, we propose a method to recover the relative pose of two robots in absolute scale and in realtime using one monocular camera on each robot. We achieve this by fusing measurements from the onboard inertial sensors on each platform with information obtained from feature correspondences between the two cameras using an Extended Kalman Filter (EKF). This forms a flexible stereo rig, providing the ability to treat the two robots as one single dynamic sensor, which can adapt to the environment and thus improve environmental mapping, obstacle avoidance and navigation. We demonstrate the power of this approach on both simulation and real datasets, employing two micro aerial vehicles (MAVs) to illustrate successful operation over general 3D motion.
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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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    CAREER: Markov Chain Monte Carlo methods for large scale correspondence problems in computer vision and robotics
    (Georgia Institute of Technology, 2011-05-17) Dellaert, Frank ; Khan, Zia ; Potthast, Christian
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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.