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Dellaert, Frank

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Author: Beall, Chris × End date: 2020 × Start date: 2010 × search.filters.applied.f.resourcesubtype: Proceedings ×

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Now showing 1 - 6 of 6
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    Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
    (Georgia Institute of Technology, 2014) Carlone, Luca ; Kira, Zsolt ; Beall, Chris ; Indelman, Vadim ; Dellaert, Frank
    Factor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those target variables. The remaining variables are essential for the formulation of the optimization problem underlying maximum a posteriori (MAP) estimation; however these variables, that we call support variables, are not strictly required as output of the estimation problem. In this paper, we propose a systematic way to abstract support variables, defining optimization problems that are only defined over the set of target variables. This abstraction naturally leads to the definition of smart factors, which correspond to constraints among target variables. We show that this perspective unifies the treatment of heterogeneous problems, ranging from structureless bundle adjustment to robust estimation in SLAM. Moreover, it enables to exploit the underlying structure of the optimization problem and the treatment of degenerate instances, enhancing both computational efficiency and robustness.
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    Large-Scale Dense 3D Reconstruction from Stereo Imagery
    (Georgia Institute of Technology, 2013-11) Alcantarilla, Pablo F. ; Beall, Chris ; Dellaert, Frank
    In this paper we propose a novel method for large-scale dense 3D reconstruction from stereo imagery. Assuming that stereo camera calibration and camera motion are known, our method is able to reconstruct accurately dense 3D models of urban environments in the form of point clouds. We take advantage of recent stereo matching techniques that are able to build dense and accurate disparity maps from two rectified images. Then, we fuse the information from multiple disparity maps into a global model by using an efficient data association technique that takes into account stereo uncertainty and performs geometric and photometric consistency validation in a multi-view setup. Finally, we use efficient voxel grid filtering techniques to deal with storage requirements in large-scale environments. In addition, our method automatically discards possible moving obstacles in the scene. We show experimental results on real video large-scale sequences and compare our approach with respect to other state-of-the-art methods such as PMVS and StereoScan.
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    Incremental Light Bundle Adjustment
    (Georgia Institute of Technology, 2012-09) Indelman, Vadim ; Roberts, Richard ; Beall, Chris ; Dellaert, Frank
    Fast and reliable bundle adjustment is essential in many applications such as mobile vision, augmented reality, and robotics. Two recent ideas to reduce the associated computational cost are structure-less SFM (structure from motion) and incremental smoothing. The former formulates the cost function in terms of multi-view constraints instead of re-projection errors, thereby eliminating the 3D structure from the optimization. The latter was developed in the SLAM (simultaneous localization and mapping) community and allows one to perform efficient incremental optimization, adaptively identifying the variables that need to be recomputed at each step. In this paper we combine these two key ideas into a computationally efficient bundle adjustment method, and additionally introduce the use of three-view constraints to remedy commonly encountered degenerate camera motions. We formulate the problem in terms of a factor graph, and incrementally update a directed junction tree which keeps track of the current best solution. Typically, only a small fraction of the camera poses are recalculated in each optimization step, leading to a significant computational gain. If desired, all or some of the observed 3D points can be reconstructed based on the optimized camera poses. To deal with degenerate motions, we use both two and three-view constraints between camera poses, which allows us to maintain a consistent scale during straight-line trajectories. We validate our approach using synthetic and real-imagery datasets and compare it to standard bundle adjustment, in terms of performance, robustness and computational cost.
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    Attitude Heading Reference System with Rotation-Aiding Visual Landmarks
    (Georgia Institute of Technology, 2012-07) Beall, Chris ; Ta, Duy-Nguyen ; Ok, Kyel ; Dellaert, Frank
    In this paper we present a novel vision-aided attitude heading reference system for micro aerial vehicles (MAVs) and other mobile platforms, which does not rely on known landmark locations or full 3D map estimation as is common in the literature. Inertial sensors which are commonly found on MAVs suffer from additive biases and noise, and yaw error will grow without bounds. The bearing-only measurements, which we call vistas, aid the vehicle’s heading estimate and allow for long-term operation while correcting for sensor drift. Our method is experimentally validated on a commercially available low-cost quadrotor MAV.
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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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    3D Reconstruction of Underwater Structures
    (Georgia Institute of Technology, 2010) Beall, Chris ; Lawrence, Brian J. ; Ila, Viorela ; Dellaert, Frank
    Environmental change is a growing international concern, calling for the regular monitoring, studying and preserving of detailed information about the evolution of underwater ecosystems. For example, fragile coral reefs are exposed to various sources of hazards and potential destruction, and need close observation. Computer vision offers promising technologies to build 3D models of an environment from two dimensional images. The state of the art techniques have enabled high-quality digital reconstruction of large-scale structures, e.g., buildings and urban environments, but only sparse representations or dense reconstruction of small objects have been obtained from underwater video and still imagery. The application of standard 3D reconstruction methods to challenging underwater environments typically produces unsatisfactory results. Accurate, full camera trajectories are needed to serve as the basis for dense 3D reconstruction. A highly accurate sparse 3D reconstruction is the ideal foundation on which to base subsequent dense reconstruction algorithms. In our application the models are constructed from synchronized high definition videos collected using a wide baseline stereo rig. The rig can be hand-held, attached to a boat, or even to an autonomous underwater vehicle. We solve this problem by employing a smoothing and mapping toolkit developed in our lab specifically for this type of application. The result of our technique is a highly accurate sparse 3D reconstruction of underwater structures such as corals.