Distributed estimation algorithms for autonomous systems

Abstract

This thesis investigates the theory and applications of distributed estimation algorithms. It was found that for specific objective functions, general meshes of distributed agents may estimate a state while maintaining a consensus over its PDF and satisfying communication/localization objective constraint at the same time. Hyperparameter tuning techniques for multiple algorithms preventing RMSE drift and communication network inefficient usage are described. An example of distributed algorithm application with rovers shows the power of such algorithms in robotics.