SLAM for structured environments using mechanically scanned imaging sonar
type: CreativeWork
description: As the modern utilization of the maritime environment only grows, uncrewed systems present the future of safety, efficiency, and capability. For submerged operations, Autonomous Underwater Vehicles (AUVs) enable scientists, industry, and militaries to access remote, inhospitable locations and execute a variety of tasks beyond the capabilities of human occupied or operated systems. Much of this autonomy relies on the vehicle having a detailed understanding of its own position. Inertial Navigation Systems provide an estimate of the distance traveled by combining numerous sensors, but are subject to unbounded error accumulation over long distances. Traditional methods of correcting for this error found in terrestrial robotics are largely unavailable in the undersea domain due to the absorption and scattering effects of electromagnetic signals in water. Acoustic communications and imaging such as Sound Navigation and Ranging (SONAR) is the most reliable and trusted method for AUVs. This thesis presents a novel method for performing Simultaneous Localization and Mapping (SLAM) through acoustic means utilizing a Mechanically Scanned Imaging Sonar (MSIS). MSIS utilize a single beam sonar mechanically rotated around the vehicle to scan a full 360◦ area. Compared with other sonar systems of similar capabilities, they require less size, weight, and power, and are available at a lower price point. The primary contribution of this thesis is a SLAM processing pipeline from MSIS to global position estimate. The pipeline extracts information from the MSIS data regarding the vehicle’s relative location compared to observed landmarks and then probabilistically matches the observed data to a best estimate vehicle position. The system is compatible with either an a priori map or a constantly updated SLAM global map. Individual beams from the MSIS are fused together into a submap. Contrast-based image processing identifies features of interest in the submap and appropriate features are…
abstract: As the modern utilization of the maritime environment only grows, uncrewed systems present the future of safety, efficiency, and capability. For submerged operations, Autonomous Underwater Vehicles (AUVs) enable scientists, industry, and militaries to access remote, inhospitable locations and execute a variety of tasks beyond the capabilities of human occupied or operated systems. Much of this autonomy relies on the vehicle having a detailed understanding of its own position. Inertial Navigation Systems provide an estimate of the distance traveled by combining numerous sensors, but are subject to unbounded error accumulation over long distances. Traditional methods of correcting for this error found in terrestrial robotics are largely unavailable in the undersea domain due to the absorption and scattering effects of electromagnetic signals in water. Acoustic communications and imaging such as Sound Navigation and Ranging (SONAR) is the most reliable and trusted method for AUVs. This thesis presents a novel method for performing Simultaneous Localization and Mapping (SLAM) through acoustic means utilizing a Mechanically Scanned Imaging Sonar (MSIS). MSIS utilize a single beam sonar mechanically rotated around the vehicle to scan a full 360◦ area. Compared with other sonar systems of similar capabilities, they require less size, weight, and power, and are available at a lower price point. The primary contribution of this thesis is a SLAM processing pipeline from MSIS to global position estimate. The pipeline extracts information from the MSIS data regarding the vehicle’s relative location compared to observed landmarks and then probabilistically matches the observed data to a best estimate vehicle position. The system is compatible with either an a priori map or a constantly updated SLAM global map. Individual beams from the MSIS are fused together into a submap. Contrast-based image processing identifies features of interest in the submap and appropriate features are…
author: Motz, Andrew J.
keywords: MSIS, SLAM, Localization
date published: 2025-09
date modified: 2026-07-27T22:05:02Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/72179

PropertyValue
property ID: DOI
value: 10.1575/1912/72179