Finding Safety in Numbers: Exploring the Possibilities of Terrorism Consequence Models as Public Safety Planning Tools
Abstract
This thesis develops a simulation framework for undersea feature-based navigation. Using an autonomous underwater vehicle (AUV) to locate an item of interest on the seafloor is a capability that would greatly benefit the Navy. AUVs provide a gateway toward removing the workforce requirement; however, they are still costly both in acquisition and maintenance. A solution to this problem is using two AUVs, one with increased capability and charged with finding and marking seafloor items with a beacon. An expendable AUV outfitted with cost-effective sensors would relocate, identify and neutralize the threat. Using undersea imaging to correlate seafloor images to an a priori image mosaic together with a ultra short baseline (USBL) beacon allows the AUV to complete challenging mission objectives without traditional navigation systems. Incremental Smoothing and Mapping 2 (iSAM2) is a Simultaneous Localization and Mapping (SLAM) technique that can be used by the AUV for position localization and is an appropriate technique, with image and USBL sensing, for real-time navigation operations. A simulation framework provides the ability to evaluate an AUVs performance while minimizing the risk of real-world operations. The framework is composed of a software architecture that allows for testing using the same software applied in real-world operations. This thesis demonstrates this framework and provides analysis for its usability for image-based SLAM.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2021
- Accession Number
- AD1164913
Entities
People
- Ryan M. Ford
Organizations
- Naval Postgraduate School