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Woods Hole Oceanographic Institution

Ryan Eustice

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Publications
»Exactly sparse extended information filters for feature-based SLAM
»Experimental Results in Synchronous-clock One-Way-Travel-time Acoustic Navigation for Autonomous Underwater Vehicles
»Visually augmented navigation for autonomous underwater vehicles
»Underwater Vehicle Navigation: Recent Advances and New Challenges
»Visually Mapping the RMS Titanic: Convervative Covariance Estimates for SLAM Information Filters
»Recent Advances in Synchronous-Clock One-Way-Travel-Time Acoustic Navigation
»Characterizing the deep insular shelf coral reef habitat of the Hind Bank marine conservation district (US Virgin Islands) using the Seabed autonomous underwater vehicle
»Visually Augmented Navigation for Autonomous Underwater Vehicles
»Towards High-Resolution Imaging from Underwater Vehicles
»Exactly Sparse Delayed-State Filters for View-Based SLAM
»Photogrammetric Models for Marine Archaeology
»A provably consistent method for imposing sparsity in feature-based SLAM information filters
»Exactly Sparse Delayed-State Filters
»Sparse Extended Information Filters: Insights into Sparsification
»Visually Navigating the RMS Titanic with SLAM Information Filters
»Large-Area Visually Augmented Navigation for Autonomous Underwater Vehicles
»Towards Bathymetry-Optimized Doppler Re-navigation for AUVs
»Advances in high-resolution imaging from underwater vehicles
»A Provably Consistent Method for Imposing Sparsity in Feature-Based SLAM Information Filters
»Advances in High-Resolution Imaging from Underwater Vehicles
»Large Area 3D Reconstructions from Underwater Surveys
»Imaging Coral I: Imaging Coral Habitats with The SeaBED AUV
»SeaBED AUV Offers New Platform for High-Resolution Imaging
»Visually Augmented Navigation in an Unstructured Environment Using a Delayed State History
»Relative Pose Estimation for Instrumented, Calibrated Imaging Platforms
»The Seabed AUV - A Platform for High Resolution Imaging
»Sensor Fusion of Structure-from-Motion, Bathymetric 3D, and Beacon-Based Navigation Modalities
»UWIT: Underwater Image Toolbox for Optical Image Processing and Mosaicking in MATLAB
»A New Autonomous Underwater Vehicle for Imaging Research
»Image Registration Underwater for Fluid Flow Measurements and Mosaicking


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M.R. Walter, R.M. Eustice, and J.J. Leonard, Exactly sparse extended information filters for feature-based SLAM, Intl. J. Robotics Research, 26 (4): 335-359, April 2007.

Abstract
Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) proposed by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF's scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained. In this paper, we propose an alternative scalable estimator based on an information form that maintains sparsity while preserving consistency. The paper describes a method for controlling the population of the information matrix, whereby we track a modified version of the SLAM posterior, essentially by ignoring a small fraction of temporal measurements. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs inference over a model that is conservative relative to the standard Gaussian distribution. We compare our algorithm to the SEIF and standard EKF both in simulation as well as on two nonlinear datasets. The results convincingly show that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the EKF.

@ARTICLE{mwalter-2007a,
author = {Matthew R. Walter and Ryan M. Eustice and John J. Leonard},
title = {Exactly sparse extended information filters for feature-based {SLAM}},
journal = {Intl. J. Robotics Research},
year = {2007},
volume = {26},
pages = {335--359},
number = {4},
month = apr,
keywords = {SEIFs, SLAM, information filter, pose-graph, sparsity},
}

FILE » mwalter-2007a.pdf



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