Navigation and Estimation Technology. Offeror s Technical Proposal entitled "Comparative Analysis of State Estimation Techniques for Mobile Robots
Abstract
OPS-Denied Navigation methods are becoming increasingly important in the development of unmanned air vehicle systems. The ability to fuse data from multiple sensors is critical in these environments and current research involves developing Bayesian filters that make navigation possible without the use of OPS. The goal of this work is to implement, evaluate, and refine state estimation techniques for mobile robots engaged in tasks such as navigation and simultaneous localization and mapping. In the context of autonomous vehicles, state estimation is a means to aid in navigation and is not the main goal. Therefore, the objective of this project is to develop, analyze, and experimentally compare state estimation strategies for autonomous vehicles based on their ability to produce high performance navigation. Bayesian estimators, including extended Kalman filters, unscented Kalman filters, particle filters and a Rao-Blackwellized particle filter, will be implemented on hardware to aid in navigation applications in OPS-Denied areas.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 07, 2024
- Source ID
- FA86512420003
Entities
People
- Matthew Hale
Organizations
- Air Force Research Laboratory
- Georgia Tech Research Corporation
- United States Air Force