Exactly Sparse Delayed-State Filters for View-Based SLAM

This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86062/1/reustice-25.pd

Towards High-resolution Imaging from Underwater Vehicles

Large area mapping at high resolution underwater continues to be constrained by sensor-level environmental constraints and the mismatch between available navigation and sensor accuracy. In this paper, advances are presented that exploit aspects of the sensing modality, and consistency and redundancy within local sensor measurements to build high-resolution optical and acoustic maps that are a consistent representation of the environment. This work is presented in the context of real-world data acquired using autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) working in diverse applications including shallow water coral reef surveys with the Seabed AUV, a forensic survey of the RMS Titanic in the North Atlantic at a depth of 4100 m using the Hercules ROV, and a survey of the TAG hydrothermal vent area in the mid-Atlantic at a depth of 3600 m using the Jason II ROV. Specifically, the focus is on the related problems of structure from motion from underwater optical imagery assuming pose instrumented calibrated cameras. General wide baseline solutions are presented for these problems based on the extension of techniques from the simultaneous localization and mapping (SLAM), photogrammetric and the computer vision communities. It is also examined how such techniques can be extended for the very different sensing modality and scale associated with multi-beam bathymetric mapping. For both the optical and acoustic mapping cases it is also shown how the consistency in mapping can be used not only for better global mapping, but also to refine navigation estimates.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86051/1/hsingh-21.pd

Large-area visually augmented navigation for autonomous underwater vehicles

Submitted to the Joint Program in Applied Ocean Science & Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2005This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source). Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. This sparsity allows for efficient O(n) update complexity in the number of images composing the view-based map by utilizing recent multilevel relaxation techniques. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsification methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the estimated state errors. Lastly, we present a novel algorithm for efficiently extracting consistent marginal covariances useful for data association from the information matrix. In summary, this thesis advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 km and 3100 m2 of mapped area). This accomplishment embodies the summed contributions of this thesis to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.This work was funded in part by the CenSSIS ERC of the National Science Foundation under grant EEC-9986821, in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation, and in part by a NDSEG Fellowship awarded through the Department of Defense

Tradeoffs in SLAM with sparse information filters

Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms. © 2008 Springer-Verlag Berlin Heidelberg

Underwater Exploration and Mapping

This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.acceptedVersio

Visually Augmented Navigation for Autonomous Underwater Vehicles

As autonomous underwater vehicles (AUVs) are becoming routinely used in an exploratory context for ocean science, the goal of visually augmented navigation (VAN) is to improve the near-seafloor navigation precision of such vehicles without imposing the burden of having to deploy additional infrastructure. This is in contrast to traditional acoustic long baseline navigation techniques, which require the deployment, calibration, and eventual recovery of a transponder network. To achieve this goal, VAN is formulated within a vision-based simultaneous localization and mapping (SLAM) framework that exploits the systems-level complementary aspects of a camera and strap-down sensor suite. The result is an environmentally based navigation technique robust to the peculiarities of low-overlap underwater imagery. The method employs a view-based representation where camera-derived relative-pose measurements provide spatial constraints, which enforce trajectory consistency and also serve as a mechanism for loop closure, allowing for error growth to be independent of time for revisited imagery. This article outlines the multisensor VAN framework and demonstrates it to have compelling advantages over a purely vision-only approach by: 1) improving the robustness of low-overlap underwater image registration; 2) setting the free gauge scale; and 3) allowing for a disconnected camera-constraint topology.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86054/1/reustice-16.pd

Toward autonomous exploration in confined underwater environments

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring- Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762), and the National Science Foundation (OCE-0955674)