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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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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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ItemPlace 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, FrankPose 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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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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ItemA Markov Chain Monte Carlo Approach to Closing the Loop in SLAM(Georgia Institute of Technology, 2005-04) Kaess, Michael ; Dellaert, FrankThe problem of simultaneous localization and mapping has received much attention over the last years. Especially large scale environments, where the robot trajectory loops back on itself, are a challenge. In this paper we introduce a new solution to this problem of closing the loop. Our algorithm is EM-based, but differs from previous work. The key is a probability distribution over partitions of feature tracks that is determined in the E-step, based on the current estimate of the motion. This virtual structure is then used in the M-step to obtain a better estimate for the motion. We demonstrate the success of our algorithm in experiments on real laser data.
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ItemThe Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue(Georgia Institute of Technology, 2002) Alegre, Fernando ; Balch, Tucker ; Berhault, Marc ; Dellaert, Frank ; Kaess, Michael ; McGuire, Robert ; Merrill, Ernest ; Moshkina, Lilia ; Ravichandran, Ram ; Walker, DanielWe describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.