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

Doctor of Philosophy

dissertationInteractive editing and manipulation of digital media is a fundamental component in digital content creation. One media in particular, digital imagery, has seen a recent increase in popularity of its large or even massive image formats. Unfortunately, current systems and techniques are rarely concerned with scalability or usability with these large images. Moreover, processing massive (or even large) imagery is assumed to be an off-line, automatic process, although many problems associated with these datasets require human intervention for high quality results. This dissertation details how to design interactive image techniques that scale. In particular, massive imagery is typically constructed as a seamless mosaic of many smaller images. The focus of this work is the creation of new technologies to enable user interaction in the formation of these large mosaics. While an interactive system for all stages of the mosaic creation pipeline is a long-term research goal, this dissertation concentrates on the last phase of the mosaic creation pipeline - the composition of registered images into a seamless composite. The work detailed in this dissertation provides the technologies to fully realize interactive editing in mosaic composition on image collections ranging from the very small to massive in scale

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

Panoramic Video Stitching

Digital camera and smartphone technologies have made high quality images and video pervasive and abundant. Combining or stitching collections of images from a variety of viewpoints into an extended panoramic image is a common and popular function for such devices. Extending this functionality to video however, poses many new challenges due to the demand for both spatial and temporal continuity. Multi-view video stitching (also called panoramic video stitching) is an emerging, common research area in computer vision, image/video processing and computer graphics and has wide applications in virtual reality, virtual tourism, surveillance, and human computer interaction. In this thesis, I will explore the technical and practical problems in the complete process of stitching a high-resolution multiview video into a high-resolution panoramic video. The challenges addressed include video stabilization, efficient multi-view video alignment and panoramic video stitching, color correction, and blurred frame detection and repair. Specifically, I propose a continuity aware Kalman filtering scheme for rotation angles for video stabilization and jitter removal. For efficient stitching of long, high-resolution panoramic videos, I propose constrained and multigrid SIFT matching schemes, concatenated image projection and warping and min-space feathering. These three approaches together can greatly reduce the computational time and memory requirement in panoramic video stitching, which makes it feasible to stitch high-resolution (e.g., 1920x1080 pixels) and long panoramic video sequences using standard workstations. Color correction is the emphasis of my research. On this topic I first performed a systematic survey and performance evaluation of nine state of the art color correction approaches in the context of two-view image stitching. My evaluation work not only gives useful insights and conclusions about the relative performance of these approaches, but also points out the remaining challenges and possible directions for future color correction research. Based on the conclusions from this evaluation work, I proposed a hybrid and scalable color correction approach for general n-view image stitching, and designed a two-view video color correction approach for panoramic video stitching. For blurred frame detection and repair, I have completed preliminary work on image partial blur detection and classification, in which I proposed a SVM-based blur block classifier using improved and new local blur features. Then, based on partial blur classification results, I designed a statistical thresholding scheme for blurred frame identification. For the detected blurred frames, I repaired them using polynomial data fitting from neighboring unblurred frames. Many of the techniques and ideas in this thesis are novel and general solutions to the technical or practical problems in panoramic video stitching. At the end of this thesis, I conclude the contributions made by this thesis to the research and popularization of panoramic video stitching, and describe those open research issues