A RAPID PROTOTYPING FRAMEWORK FOR VISION-BASED LOCALIZATION ALGORITHMS

Unmanned aerial vehicles (UAVs) and unmanned surface vessels (USVs) traditionally rely on the Global Positioning System (GPS). As adversarial electronic warfare capabilities grow, GPS signals face ever-increasing risks of jamming and spoofing, especially in contested environments. Therefore, as these adversarial risks increase, alternatives to GPS have become necessary. Vision-based navigation and localization algorithms, which estimate positional data using an onboard camera sensor, offer a promising solution. To accelerate the development of such an approach, a simulation tool is necessary for rapidly prototyping and evaluating these algorithms before implementation in a realistic environment. This research introduced an open-source simulation framework developed using the Robot Operating System (ROS) and Gazebo. Within the simulation, a UAV equipped with an onboard camera and a YOLO11 convolutional neural network (CNN) detected and tracked both a friendly and an enemy vessel. A particle filter then estimated relative positional data, enabling decision-making without GPS. The framework supported plug-and-play integration and modification of estimators, object detection models, and sensor parameters, allowing for future development and expansion. This work demonstrated the feasibility and various challenges of vision-based localization, laying a foundation for real-world testing of autonomous systems operating in GPS-denied environments.

Department dc:contributor.department

Mechanical and Aerospace Engineering (MAE)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2025

Author dc:creator

• Zukowski, Braden E.

Advisor dc:contributor.advisor

• Kragelund, Sean P.

Statement dc:rights

• This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.

Handle dc:identifier.uri

https://hdl.handle.net/10945/74230

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/74230