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Dellaert, Frank

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Publication Search Results

Now showing 1 - 10 of 174
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    Factor Graphs for Action
    (Georgia Institute of Technology, 2021-08-25) Dellaert, Frank
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    Career Options in Robotics: Academia vs Industry
    (Georgia Institute of Technology, 2021-02-17) Collins, Thomas R. ; Coogan, Samuel ; Dellaert, Frank ; Mazumdar, Anirban ; Parikh, Anup ; Young, Aaron
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    Applying Emerging Technologies In Service of Journalism at The New York Times
    ( 2020-10-30) Boonyapanachoti, Woraya (Mint) ; Dellaert, Frank ; Essa, Irfan ; Fleisher, Or ; Kanazawa, Angjoo ; Lavallee, Marc ; McKeague, Mark ; Porter, Lana Z.
    Emerging technologies, particularly within computer vision, photogrammetry, and spatial computing, are unlocking new forms of storytelling for journalists to help people understand the world around them. In this talk, members of the R&D team at The New York Times talk about their process for researching and developing new capabilities built atop emerging research. In particular, hear how they are embracing photogrammetry and spatial computing to create new storytelling techniques that allow a reader to experience an event as close to reality as possible. Learn about the process of collecting photos, generating 3D models, editing, and technologies used to scale up to millions of readers. The team will also share their vision for these technologies and journalism, their ethical considerations along the way, and a research wishlist that would accelerate their work. In its 169 year history, The New York Times has evolved with new technologies, publishing its first photo in 1896 with the rise of cameras, introducing the world’s first computerized news retrieval system in 1972 with the rise of the computer, and launching a website in 1996 with the rise of the internet. Since then, the pace of innovation has accelerated alongside the rise of smartphones, cellular networks, and other new technologies. The Times now has the world’s most popular daily podcast, a new weekly video series, and award-winning interactive graphics storytelling. Join us for a discussion about how our embrace of emerging technologies is helping us push the boundaries of journalism in 2020 and beyond.
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    Factor Graphs for Flexible Inference in Robotics and Vision
    (Georgia Institute of Technology, 2019-01-09) Dellaert, Frank
    In robotics and computer vision, Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I will review how SLAM, SFM and other problems in robotics and vision can be posed in terms of factor graphs, which provide a graphical language in which to develop and collaborate on such problems. The theme of the talk will be to emphasize the advantages and intuition that come with that. I will show how using these insights we have developed both batch and incremental algorithms defined on graphs in the SLAM/SFM domain, as well as more sophisticated approaches to trajectory optimization. Many of these ideas are embodied in the Skydio R1, a commercially available, fully autonomous drone I helped develop at Skydio, a San Francisco Bay area startup.
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    Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation
    (Georgia Institute of Technology, 2015-05-30) Forster, Christian ; Carlone, Luca ; Dellaert, Frank ; Scaramuzza, Davide
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    Duality-based Verification Techniques for 2D SLAM
    (Georgia Institute of Technology, 2015-05) Carlone, Luca ; Dellaert, Frank
    While iterative optimization techniques for Simultaneous Localization and Mapping (SLAM) are now very efficient and widely used, none of them can guarantee global convergence to the maximum likelihood estimate. Local convergence usually implies artifacts in map reconstruction and large localization errors, hence it is very undesirable for applications in which accuracy and safety are of paramount importance. We provide a technique to verify if a given 2D SLAM solution is globally optimal. The insight is that, while computing the optimal solution is hard in general, duality theory provides tools to compute tight bounds on the optimal cost, via convex programming. These bounds can be used to evaluate the quality of a SLAM solution, hence providing a “sanity check” for state-of-the-art incremental and batch solvers. Experimental results show that our technique successfully identifies wrong estimates (i.e., local minima) in large-scale SLAM scenarios. This work, together with [1], represents a step towards the objective of having SLAM techniques with guaranteed performance, that can be used in safety-critical applications.
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    Differential Dynamic Programming for Optimal Estimation
    (Georgia Institute of Technology, 2015-05) Kobilarov, Marin ; Ta, Duy-Nguyen ; Dellaert, Frank
    This paper studies an optimization-based approach for solving optimal estimation and optimal control problems through a unified computational formulation. The goal is to perform trajectory estimation over extended past horizons and model-predictive control over future horizons by enforcing the same dynamics, control, and sensing constraints in both problems, and thus solving both problems with identical computational tools. Through such systematic estimation-control formulation we aim to improve the performance of autonomous systems such as agile robotic vehicles. This work focuses on sequential sweep trajectory optimization methods, and more specifically extends the method known as differential dynamic programming to the parameter-dependent setting in order to enable the solutions to general estimation and control problems.
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    Initialization Techniques for 3D SLAM: A Survey on Rotation Estimation and its Use in Pose Graph Optimization
    (Georgia Institute of Technology, 2015-05) Carlone, Luca ; Tron, Roberto ; Daniilidis, Kostas ; Dellaert, Frank
    Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotations are the actual reason why SLAM is a difficult problem, in this work we survey techniques for 3D rotation estimation. Rotation estimation has a rich history in three scientific communities: robotics, computer vision, and control theory. We review relevant contributions across these communities, assess their practical use in the SLAM domain, and benchmark their performance on representative SLAM problems (Fig. 1). We show that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.
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    Monocular Image Space Tracking on a Computationally Limited MAV
    (Georgia Institute of Technology, 2015-05) Ok, Kyel ; Gamage, Dinesh ; Drummond, Tom ; Dellaert, Frank ; Roy, Nicholas
    We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations.The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy.
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    Information-based Reduced Landmark SLAM
    (Georgia Institute of Technology, 2015-05) Choudhary, Siddharth ; Indelman, Vadim ; Christensen, Henrik I. ; Dellaert, Frank
    In this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate without compromising the accuracy of the estimated trajectory. We also propose an incremental version of the reduction algorithm which can be used in SLAM framework resulting in information based reduced landmark SLAM. The results of reduced landmark based SLAM algorithm are shown on Victoria park dataset and a Synthetic dataset and are compared with standard graph SLAM (SAM [6]) algorithm. We demonstrate a reduction of 40-50% in the number of landmarks and around 55% in the number of poses with minimal estimation error as compared to standard SLAM algorithm.