Dellaert, Frank

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Now showing 1 - 10 of 10
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    iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering
    (Georgia Institute of Technology, 2011) Kaess, Michael ; Johannsson, Hordur ; Roberts, Richard ; Ila, Viorela ; Leonard, John ; Dellaert, Frank
    We present iSAM2, a fully incremental, graphbased version of incremental smoothing and mapping (iSAM). iSAM2 is based on a novel graphical model-based interpretation of incremental sparse matrix factorization methods, afforded by the recently introduced Bayes tree data structure. The original iSAM algorithm incrementally maintains the square root information matrix by applying matrix factorization updates. We analyze the matrix updates as simple editing operations on the Bayes tree and the conditional densities represented by its cliques. Based on that insight, we present a new method to incrementally change the variable ordering which has a large effect on efficiency. The efficiency and accuracy of the new method is based on fluid relinearization, the concept of selectively relinearizing variables as needed. This allows us to obtain a fully incremental algorithm without any need for periodic batch steps. We analyze the properties of the resulting algorithm in detail, and show on various real and simulated datasets that the iSAM2 algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
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    Flow Separation for Fast and Robust Stereo Odometry
    (Georgia Institute of Technology, 2009-05) Kaess, Michael ; Ni, Kai ; Dellaert, Frank
    Separating sparse flow provides fast and robust stereo visual odometry that deals with nearly degenerate situations that often arise in practical applications.We make use of the fact that in outdoor situations different constraints are provided by close and far structure, where the notion of close depends on the vehicle speed. The motion of distant features determines the rotational component that we recover with a robust two-point algorithm. Once the rotation is known, we recover the translational component from close features using a robust one-point algorithm. The overall algorithm is faster than estimating the motion in one step by a standard RANSAC-based three-point algorithm. And in contrast to other visual odometry work, we avoid the problem of nearly degenerate data, under which RANSAC is known to return inconsistent results. We confirm our claims on data from an outdoor robot equipped with a stereo rig.
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    Place Recognition-Based Fixed-Lag Smoothing for Environments with Unreliable GPS
    (Georgia Institute of Technology, 2008-05) Mottaghi, Roozbeh ; Kaess, Michael ; Ranganathan, Ananth ; Roberts, Richard ; Dellaert, Frank
    Pose estimation of outdoor robots presents some distinct challenges due to the various uncertainties in the robot sensing and action. In particular, global positioning sensors of outdoor robots do not always work perfectly, causing large drift in the location estimate of the robot. To overcome this common problem, we propose a new approach for global localization using place recognition. First, we learn the location of some arbitrary key places using odometry measurements and GPS measurements only at the start and the end of the robot trajectory. In subsequent runs, when the robot perceives a key place, our fixed-lag smoother fuses odometry measurements with the relative location to the key place to improve its pose estimate. Outdoor mobile robot experiments show that place recognition measurements significantly improve the estimate of the smoother in the absence of GPS measurements.
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    iSAM: Incremental Smoothing and Mapping
    (Georgia Institute of Technology, 2008) Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank
    We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real-time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.
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    Fast 3D Pose Estimation With Out-of-Sequence Measurements
    (Georgia Institute of Technology, 2007-10) Ranganathan, Ananth ; Kaess, Michael ; Dellaert, Frank
    We present an algorithm for pose estimation using fixed-lag smoothing. We show that fixed-lag smoothing enables inclusion of measurements from multiple asynchronous measurement sources in an optimal manner. Since robots usually have a plurality of uncoordinated sensors, our algorithm has an advantage over filtering-based estimation algorithms, which cannot incorporate delayed measurements optimally. We provide an implementation of the general fixed-lag smoothing algorithm using square root smoothing, a technique that has recently become prominent. Square root smoothing uses fast sparse matrix factorization and enables our fixed-lag pose estimation algorithm to run at upwards of 20 Hz. Our algorithm has been extensively tested over hundreds of hours of operation on a robot operating in outdoor environments. We present results based on these tests that verify our claims using wheel encoders, visual odometry, and GPS as sensors.
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    iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association
    (Georgia Institute of Technology, 2007-04) Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank
    We introduce incremental smoothing and mapping (iSAM), a novel approach to the problem of simultaneous localization and mapping (SLAM) that addresses the data association problem and allows real-time application in large-scale environments. We employ smoothing to obtain the complete trajectory and map without the need for any approximations, exploiting the natural sparsity of the smoothing information matrix. A QR-factorization of this information matrix is at the heart of our approach. It provides efficient access to the exact covariances as well as to conservative estimates that are used for online data association. It also allows recovery of the exact trajectory and map at any given time by backsubstitution. Instead of refactoring in each step, we update the QR-factorization whenever a new measurement arrives. We analyze the effect of loops, and show how our approach extends to the non-linear case. Finally, we provide experimental validation of the overall non-linear algorithm based on the standard Victoria Park data set with unknown correspondences.
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    Fast Incremental Square Root Information Smoothing
    (Georgia Institute of Technology, 2007-01) Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank
    We propose a novel approach to the problem of simultaneous localization and mapping (SLAM) based on incremental smoothing, that is suitable for real-time applications in large-scale environments. The main advantages over filter-based algorithms are that we solve the full SLAM problem without the need for any approximations, and that we do not suffer from linearization errors. We achieve efficiency by updating the square-root information matrix, a factored version of the naturally sparse smoothing information matrix. We can efficiently recover the exact trajectory and map at any given time by back-substitution. Furthermore, our approach allows access to the exact covariances, as it does not suffer from under-estimation of uncertainties, which is another problem inherent to filters. We present simulation-based results for the linear case, showing constant time updates for exploration tasks. We further evaluate the behavior in the presence of loops, and discuss how our approach extends to the non-linear case. Finally, we evaluate the overall non-linear algorithm on the standard Victoria Park data set.
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    Loopy SAM
    (Georgia Institute of Technology, 2007-01) Ranganathan, Ananth ; Kaess, Michael ; Dellaert, Frank
    Smoothing approaches to the Simultaneous Localization and Mapping (SLAM) problem in robotics are superior to the more common filtering approaches in being exact, better equipped to deal with non-linearities, and computing the entire robot trajectory. However, while filtering algorithms that perform map updates in constant time exist, no analogous smoothing method is available. We aim to rectify this situation by presenting a smoothing-based solution to SLAM using Loopy Belief Propagation (LBP) that can perform the trajectory and map updates in constant time except when a loop is closed in the environment. The SLAM problem is represented as a Gaussian Markov Random Field (GMRF) over which LBP is performed. We prove that LBP, in this case, is equivalent to Gauss-Seidel relaxation of a linear system. The inability to compute marginal covariances efficiently in a smoothing algorithm has previously been a stumbling block to their widespread use. LBP enables the efficient recovery of the marginal covariances, albeit approximately, of landmarks and poses. While the final covariances are overconfident, the ones obtained from a spanning tree of the GMRF are conservative, making them useful for data association. Experiments in simulation and using real data are presented.
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    A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM
    (Georgia Institute of Technology, 2005-04) Kaess, Michael ; Dellaert, Frank
    The problem of simultaneous localization and mapping has received much attention over the last years. Especially large scale environments, where the robot trajectory loops back on itself, are a challenge. In this paper we introduce a new solution to this problem of closing the loop. Our algorithm is EM-based, but differs from previous work. The key is a probability distribution over partitions of feature tracks that is determined in the E-step, based on the current estimate of the motion. This virtual structure is then used in the M-step to obtain a better estimate for the motion. We demonstrate the success of our algorithm in experiments on real laser data.
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    Reconstruction of Objects with Jagged Edges through Rao-Blackwellized Fitting of Piecewise Smooth Subdivision Curves
    (Georgia Institute of Technology, 2003-10) Kaess, Michael ; Dellaert, Frank
    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 corners to model non-smooth features along otherwise smooth curves. A Markov chain Monte Carlo approach yields an approximate posterior distribution over tags, while Rao-Blackwellization allows us to integrate out the control point locations by an approximation. We apply this general methodology to multi-view reconstruction of piecewise smooth curves from multiple calibrated views in which the object has been segmented from the background. Results are shown for multiple images of two pot shards as would be encountered in archaeological applications.