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Dellaert,
Frank
Dellaert,
Frank
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ItemConcurrent Filtering and Smoothing: A Parallel Architecture for Real-Time Navigation and Full Smoothing(Georgia Institute of Technology, 2014) Williams, Stephen ; Indelman, Vadim ; Kaess, Michael ; Roberts, Richard ; Leonard, John J. ; Dellaert, FrankWe present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of Factor Graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.
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ItemAutonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors(Georgia Institute of Technology, 2013-04) Wu, Allen D. ; Johnson, Eric N. ; Kaess, Michael ; Dellaert, Frank ; Chowdhary, GirishA vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied environments is presented. Particularly, feature point information from a monocular vision sensor are used to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS scenario verify the presented method.
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ItemOnline Probabilistic Topological Mapping(Georgia Institute of Technology, 2011-01-24) Ranganathan, Ananth ; Dellaert, FrankWe present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao–Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.
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ItemBinding Balls: Fast Detection of Binding Sites Using a Property of Spherical Fourier Transform(Georgia Institute of Technology, 2009) Comin, Matteo ; Guerra, Concettina ; Dellaert, FrankThe functional prediction of proteins is one of the most challenging problems in modern biology. An established computational technique involves the identification of threedimensional local similarities in proteins. In this article, we present a novel method to quickly identify promising binding sites. Our aim is to efficiently detect putative binding sites without explicitly aligning them. Using the theory of Spherical Harmonics, a candidate binding site is modeled as a Binding Ball. The Binding Ball signature, offered by the Spherical Fourier coefficients, can be efficiently used for a fast detection of putative regions. Our contribution includes the Binding Ball modeling and the definition of a scoring function that does not require aligning candidate regions. Our scoring function can be computed efficiently using a property of Spherical Fourier transform (SFT) that avoids the evaluation of all alignments. Experiments on different ligands show good discrimination power when searching for known binding sites. Moreover, we prove that this method can save up to 40% in time compared with traditional approaches.
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ItemSquare Root SAM Simultaneous Localization and Mapping via Square Root Information Smoothing(Georgia Institute of Technology, 2006) Dellaert, Frank ; Kaess, MichaelSolving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper we present the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. We present both simulation results and actual SLAM experiments in large-scale environments that underscore the potential of these methods as an alternative to EKF-based approaches.
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ItemEM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence(Georgia Institute of Technology, 2003) Dellaert, Frank ; Seitz, Steven M. ; Thorpe, Charles E. ; Thrun, SebastianLearning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning and the data association problem in parallel. The algorithm is developed in the context of the the structure from motion problem, which is the problem of estimating a 3D scene model from a collection of image data. To accommodate the spatial constraints in this domain, we compute virtual measurements as sufficient statistics to be used in the M-step. We develop an efficient Markov chain Monte Carlo sampling method called chain flipping, to calculate these statistics in the E-step. Experimental results show that we can solve hard data association problems when learning models of 3D scenes, and that we can do so efficiently. We conjecture that this approach can be applied to a broad range of model learning problems from sensor data, such as the robot mapping problem.