Dellaert, Frank

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Now showing 1 - 10 of 14
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    DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping
    (Georgia Institute of Technology, 2013-05) Cunningham, Alexander ; Indelman, Vadim ; Dellaert, Frank
    This paper presents an consistent decentralized data fusion approach for robust multi-robot SLAM in dan- gerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neigh- borhood information in a single, consistent augmented local map, without the overly conservative approach to avoiding information double-counting in the previous DDF-SAM algo- rithm. We introduce the anti-factor as a means to subtract information in graphical SLAM systems, and illustrate its use to both replace information in an incremental solver and to cancel out neighborhood information from shared summarized maps. This paper presents and compares three summarization techniques, with two exact approaches and an approximation. We evaluated the proposed system in a synthetic example and show the augmented local system and the associated summarization technique do not double-count information, while keeping performance tractable.
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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.
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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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    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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    Stereo Tracking and Three-Point/One-Point Algorithms - A Robust Approach in Visual Odometry
    (Georgia Institute of Technology, 2006-10) Ni, Kai ; Dellaert, Frank
    In this paper, we present an approach of calculating visual odometry for outdoor robots equipped with a stereo rig. Instead of the typical feature matching or tracking, we use an improved stereo-tracking method that simultaneously decides the feature displacement in both cameras. Based on the matched features, a three-point algorithm for the resulting quadrifocal setting is carried out in a RANSAC framework to recover the unknown odometry. In addition, the change in rotation can be derived from infinity homography, and the remaining translational unknowns can be obtained even faster consequently . Both approaches are quite robust and deal well with challenging conditions such as wheel slippage.
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    A Rao-Blackwellized Particle Filter for Topological Mapping
    (Georgia Institute of Technology, 2006-05) Ranganathan, Ananth ; Dellaert, Frank
    We present a particle filtering algorithm to construct topological maps of an uninstrument environment. The algorithm presented here constructs the posterior on the space of all possible topologies given measurements, and is based on our previous work on a Bayesian inference framework for topological maps [21]. Constructing the posterior solves the perceptual aliasing problem in a general, robust manner. The use of a Rao-Blackwellized Particle Filter (RBPF) for this purpose makes the inference in the space of topologies incremental and run in real-time. The RBPF maintains the joint posterior on topological maps and locations of landmarks. We demonstrate that, using the landmark locations thus obtained, the global metric map can be obtained from the topological map generated by our algorithm through a simple post-processing step. A data-driven proposal is provided to overcome the degeneracy problem inherent in particle filters. The use of a Dirichlet process prior on landmark labels is also a novel aspect of this work. We use laser range scan and odometry measurements to present experimental results on a robot.
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    Data-Driven MCMC for Learning and Inference in Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2005-07) Oh, Sang Min ; Rehg, James M. ; Balch, Tucker ; Dellaert, Frank
    Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM, but inference in SLDS models is computationally intractable. This paper describes a novel inference algorithm for SLDS models based on the Data- Driven MCMC paradigm. We describe a new proposal distribution which substantially increases the convergence speed. Comparisons to standard deterministic approximation methods demonstrate the improved accuracy of our new approach. We apply our approach to the problem of learning an SLDS model of the bee dance. Honeybees communicate the location and distance to food sources through a dance that takes place within the hive. We learn SLDS model parameters from tracking data which is automatically extracted from video. We then demonstrate the ability to successfully segment novel bee dances into their constituent parts, effectively decoding the dance of the bees.
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    Mixture Trees for Modeling and Fast Conditional Sampling with Applications in Vision and Graphics
    (Georgia Institute of Technology, 2005-06) Dellaert, Frank ; Kwatra, Vivek ; Oh, Sang Min
    We introduce mixture trees, a tree-based data-structure for modeling joint probability densities using a greedy hierarchical density estimation scheme. We show that the mixture tree models data efficiently at multiple resolutions, and present fast conditional sampling as one of many possible applications. In particular, the development of this datastructure was spurred by a multi-target tracking application, where memory-based motion modeling calls for fast conditional sampling from large empirical densities. However, it is also suited to applications such as texture synthesis, where conditional densities play a central role. Results will be presented for both these applications.
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    A Multi-Camera Pose Tracker for Assisting the Visually Impaired
    (Georgia Institute of Technology, 2005-06) Dellaert, Frank ; Tariq, Sarah
    6DOF Pose tracking is useful in many contexts, e.g., in augmented reality (AR) applications. In particular, we seek to assist visually impaired persons by providing them with an auditory interface to their environment through sonification. For this purpose, accurate head tracking in mixed indoor/outdoor settings is the key enabling technology. Most of the work to date has concentrated on single-camera systems with a relatively small field of view, but this presents a fundamental limit on the accuracy of such systems. We present a multi-camera pose tracker that handles an arbitrary configuration of cameras rigidly fixed to the object of interest. By using multiple cameras, we increase both the robustness and the accuracy by which a 6-DOF pose is tracked. However, in a multi-camera rig setting, earlier methods for determining the unknown pose from three world-to-camera correspondences are no longer applicable, as they all assume a common center of projection. In this paper, we present a RANSAC-based method that copes with this limitation and handles multi-camera rigs. In addition, we present quantitative results to serve as a design guide for full system deployments based on multi-camera rigs. Our formulation is completely general, in that it handles an arbitrary, heterogeneous collection of cameras in any arbitrary configuration.