Dellaert, Frank

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Now showing 1 - 10 of 37
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    GroupSAC: Efficient Consensus in the Presence of Groupings
    (Georgia Institute of Technology, 2009-09) Ni, Kai ; Jin, Hailin ; Dellaert, Frank
    We present a novel variant of the RANSAC algorithm that is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based on which we introduce a new binomial mixture model rather than the simple binomial model as used in RANSAC. We prove that in the new model it is more efficient to sample data from a smaller numbers of groups and groups with more tentative correspondences, which leads to a new sampling procedure that uses progressive numbers of groups. We demonstrate our algorithm on two classical geometric vision problems: wide-baseline matching and camera resectioning. The experiments show that the algorithm serves as a general framework that works well with three possible grouping strategies investigated in this paper, including a novel optical flow based clustering approach. The results show that our algorithm is able to achieve a significant performance gain compared to the standard RANSAC and PROSAC.
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    Bayesian Surprise and Landmark Detection
    (Georgia Institute of Technology, 2009-05) Ranganathan, Ananth ; Dellaert, Frank
    Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. Our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. We evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
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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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    Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies
    (Georgia Institute of Technology, 2009) Roberts, Richard ; Potthast, Christian ; Dellaert, Frank
    This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging system through an environment that we assume to have some degree of statistical regularity. For example, in autonomous ground vehicles the structure of the environment around the vehicle is far from arbitrary, and the depth at each pixel is often approximately constant. The subspace constraints hold not only for perspective cameras, but in fact for a very general class of imaging systems, including catadioptric and multiple-view systems. Using minimal assumptions about the imaging system, we learn a probabilistic subspace constraint that captures the statistical regularity of the scene geometry relative to an imaging system. We propose an extension to probabilistic PCA (Tipping and Bishop, 1999) as a way to robustly learn this subspace from recorded imagery, and demonstrate its use in conjunction with a sparse optical flow algorithm. To deal with the sparseness of the input flow, we use a generative model to estimate the subspace using only the observed flow measurements. Additionally, to identify and cope with image regions that violate subspace constraints, such as moving objects, objects that violate the depth regularity, or gross flow estimation errors, we employ a per-pixel Gaussian mixture outlier process. We demonstrate results of finding the optical flow subspaces and employing them to estimate dense flow and to recover camera motion for a variety of imaging systems in several different environments.
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    Detecting and Matching Repeated Patterns for Automatic Geo-tagging in Urban Environments
    (Georgia Institute of Technology, 2008-06) Schindler, Grant ; Krishnamurthy, Panchapagesan ; Lublinerman, Roberto ; Liu, Yanxi ; Dellaert, Frank
    We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.
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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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    Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction
    (Georgia Institute of Technology, 2007-10) Ni, Kai ; Steedly, Drew ; Dellaert, Frank
    Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However, traditional bundle adjustment algorithms require a considerable amount of memory and computational resources. In this paper, we present an extremely efficient, inherently out-of-core bundle adjustment algorithm. We decouple the original problem into several submaps that have their own local coordinate systems and can be optimized in parallel. A key contribution to our algorithm is making as much progress towards optimizing the global non-linear cost function as possible using the fragments of the reconstruction that are currently in core memory. This allows us to converge with very few global sweeps (often only two) through the entire reconstruction. We present experimental results on large-scale 3D reconstruction datasets, both synthetic and real.
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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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    Inferring Temporal Order of Images From 3D Structure
    (Georgia Institute of Technology, 2007-06) Schindler, Grant ; Dellaert, Frank ; Kang, Sing Bing
    In this paper, we describe a technique to temporally sort a collection of photos that span many years. By reasoning about persistence of visible structures, we show how this sorting task can be formulated as a constraint satisfaction problem (CSP). Casting this problem as a CSP allows us to efficiently find a suitable ordering of the images despite the large size of the solution space (factorial in the number of images) and the presence of occlusions. We present experimental results for photographs of a city acquired over a one hundred year period.
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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.