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Dellaert, Frank

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Author: Indelman, Vadim × Author: Nelson, Erik × End date: 2020 × Start date: 2010 × search.filters.applied.f.resourcesubtype: Proceedings ×

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    Distributed Real-time Cooperative Localization and Mapping Using an Uncertainty-Aware Expectation Maximization Approach
    (Georgia Institute of Technology, 2015-05) Dong, Jing ; Nelson, Erik ; Indelman, Vadim ; Michael, Nathan ; Dellaert, Frank
    We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment sing indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.
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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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    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.