ICRA 2011 Paper Abstract

Close

Paper TuP1-InteracInterac.3

Zeng, Wenwu (Harbin Institute of Technology, Shenzhen Graduate School), Zhu, Xiaorui (Harbin Institute of Technology Shenzhen Graduate School), Li, Zexiang (HKUST)

Less Computational Unscented Kalman Filter for Practical State Estimation of Small Scale Unmanned Helicopters

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Sensor Fusion, Autonomous Navigation

Abstract

This paper presents the unscented Kalman filter (UKF) with reduced simplex sigma-point for the navigation system in a small scale unmanned helicopter. UKF is widely applied to nonlinear systems. However, the disadvantage of traditional UKF is the high computational cost caused by the unscented transformation step. The computational cost is proportional to the number of the constructed sigma-points. Therefore a reduced simplex sigma-point selection is proposed to be practically applied for the sensor fusion on the unmanned helicopter. The simulation and experimental results verify the computational load reduction.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-03-30  00:54:55 PST  Terms of use