Dellaert, Frank

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Now showing 1 - 2 of 2
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    Probabilistic Structure Matching for Visual SLAM with a Multi-Camera Rig
    (Georgia Institute of Technology, 2010-02) Kaess, Michael ; Dellaert, Frank
    We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion. Our approach circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm. In the expectation step we determine a distribution over correspondences by sampling. In the maximization step, we find optimal parameters of a density over the robot motion and environment structure. By summarizing the sampling results in so-called virtual measurements, the resulting optimization simplifies to the equivalent optimization problem for known correspondences. We present results for simulated data, as well as for data obtained by a mobile robot equipped with a multi-camera rig.
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    Covariance Recovery from a Square Root Information Matrix for Data Association
    (Georgia Institute of Technology, 2009) Kaess, Michael ; Dellaert, Frank
    Data association is one of the core problems of simultaneous localization and mapping (SLAM), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. However, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. We present a dynamic programming algorithm for efficient recovery of the marginal covariances needed for data association. As input we use a square root information matrix as maintained by our incremental smoothing and mapping (iSAM) algorithm. The contributions beyond our previous work are an improved algorithm for recovering the marginal covariances and a more thorough treatment of data association now including the joint compatibility branch and bound (JCBB) algorithm. We further show how to make information theoretic decisions about measurements before actually taking the measurement, therefore allowing a reduction in estimation complexity by omitting uninformative measurements. We evaluate our work on simulated and real-world data.