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Title:Feedback Particle Filter Algorithm for Simultaneous Localization and Map Building
Author(s):Huang, Geng
Contributor(s):Mehta, Prashant G.
robotic control
robot motion planning
simultaneous localization and map building
optimal control
particle filters
feedback particle filters
autonomous vehicle localization
Abstract:In this thesis, feedback-particle-filter-based algorithms to solve the simultaneous localization and map building (SLAM) problem are developed. Feedback particle filter is a new formulation of the particle filter for the nonlinear filtering problem based on the concepts from optimal control and mean-field game theory. A software package is also developed. The goal of SLAM is to compute the absolute location of a vehicle, starting from an unknown location in an unknown environment by incrementally building a map for the environment. Multiple landmarks are used to represent the environment. Based on measurements of relative positions between the landmarks and the vehicle, the absolute position of the vehicle is estimated simultaneously while localizing the landmarks.
Issue Date:2012-05
Publication Status:unpublished
Sponsor:National Science Foundation (NSF)
Air Force Office of Scientific Research (AFOSR)
Date Available in IDEALS:2014-01-09

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