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Sigma-Point Filters: An Overview with Applications to Integrated Navigation and Vision Assisted Control

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1 Author(s)
Eric Wan ; Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health & Science University

In this presentation, we first provide an overview of Sigma-Point filtering methods, which include the Unscented Kalman Filter (UKF), Central Difference Kalman Filter (CDKF), and several variants with hybrid extensions to sequential Monte Carlo filtering (e.g., particle filtering). In the second half, we focus on recent applications to integrated navigation systems (INS), which provide state-estimation by combining GPS and inertial measurements. In addition, we present new work on using video data to extract the equivalent state-information (i.e., replacing the INS) for use in closed-loop control of an Unmanned Aerial Vehicle (UAV).

Published in:

Nonlinear Statistical Signal Processing Workshop, 2006 IEEE

Date of Conference:

13-15 Sept. 2006