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Dellaert, Frank

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Author: Indelman, Vadim × End date: 2014 × Start date: 2014 × search.filters.applied.f.resourcesubtype: Proceedings ×

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Now showing 1 - 5 of 5
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    Incremental Distributed Robust Inference from Arbitrary Robot Poses via EM and Model Selection
    (Georgia Institute of Technology, 2014-07) Indelman, Vadim ; Michael, Nathan ; Dellaert, Frank
    We present a novel approach for multi-robot distributed and incremental inference over variables of interest, such as robot trajectories, considering the initial relative poses between the robots and multi-robot data association are both unknown. Assuming robots share with each other informative observations, this inference problem is formulated within an Expectation-Maximization (EM) optimization, performed by each robot separately, alternating between inference over variables of interest and multi-robot data association. To facilitate this process, a common reference frame between the robots should first be established. We show the latter is coupled with determining multi-robot data association, and therefore concurrently infer both using a separate EM optimization. This optimization is performed by each robot starting from several promising initial solutions, converging to locally-optimal hypotheses regarding data association and reference frame transformation. Choosing the best hypothesis in an incremental problem setting is in particular challenging due to high sensitivity to perceptual aliasing and possibly insufficient amount of data. Selecting an incorrect hypothesis introduces outliers and can lead to catastrophic results. To address these challenges we develop a model-selection based approach to choose the most probable hypothesis and use the Chinese restaurant process to disambiguate the hypotheses prior probabilities over time.
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    An Experimental Study of Robust Distributed Multi-Robot Data Association from Arbitrary Poses
    (Georgia Institute of Technology, 2014-06) Nelson, Erik ; Indelman, Vadim ; Michael, Nathan ; Dellaert, Frank
    In this work, we experimentally investigate the problem of computing the relative transformation between multiple vehicles from corresponding interrobot observations during autonomous operation in a common unknown environment. Building on our prior work, we consider an EM-based methodology which evaluates sensory observations gathered over vehicle trajectories to establish robust relative pose transformations between robots. We focus on experimentally evaluating the performance of the approach as well as its computational complexity and shared data requirements using multiple autonomous vehicles (aerial robots). We describe an observation subsampling technique which utilizes laser scan autocovariance to reduce the total number of observations shared between robots. Employing this technique reduces run time of the algorithm significantly, while only slightly diminishing the accuracies of computed inter-robot transformations. Finally, we provide discussion on data transfer and the feasibility of implementing the approach on a mesh network.
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    Planning Under Uncertainty in the Continuous Domain: A Generalized Belief Space Approach
    (Georgia Institute of Technology, 2014) Indelman, Vadim ; Carlone, Luca ; Dellaert, Frank
    This work investigates the problem of planning under uncertainty, with application to mobile robotics. We propose a probabilistic framework in which the robot bases its decisions on the generalized belief , which is a probabilistic description of its own state and of external variables of interest. The approach naturally leads to a dual-layer architecture: an inner estimation layer, which performs inference to predict the outcome of possible decisions, and an outer decisional layer which is in charge of deciding the best action to undertake. The approach does not discretize the state or control space, and allows planning in continuous domain. Moreover, it allows to relax the assumption of maximum likelihood observations: predicted measurements are treated as random variables and are not considered as given. Experimental results show that our planning approach produces smooth trajectories while maintaining uncertainty within reasonable bounds.
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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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    Multi-Robot Pose Graph Localization and Data Association from Unknown Initial Relative Poses via Expectation Maximization
    (Georgia Institute of Technology, 2014) Indelman, Vadim ; Nelson, Erik ; Michael, Nathan ; Dellaert, Frank
    This paper presents a novel approach for multi- robot pose graph localization and data association without requiring prior knowledge about the initial relative poses of the robots. Without a common reference frame, the robots can only share observations of interesting parts of the environment, and trying to match between observations from different robots will result in many outlier correspondences. Our approach is based on the following key observation: while each multi-robot correspondence can be used in conjunction with the local robot estimated trajectories, to calculate the transformation between the robot reference frames, only the inlier correspondences will be similar to each other. Using this concept, we develop an expectation-maximization (EM) approach to efficiently infer the robot initial relative poses and solve the multi-robot data association problem. Once this transformation between the robot reference frames is estimated with sufficient measure of confidence, we show that a similar EM formulation can be used to solve also the full multi-robot pose graph problem with unknown multi-robot data association. We evaluate the performance of the developed approach both in a statistical synthetic-environment study and in a real-data experiment, demonstrating its robustness to high percentage of outliers.