TY - GEN
T1 - Pose estimation of a rigid body and its supporting moving platform using two gyroscopes and relative complementary measurements
AU - Zhang, Yizhai
AU - Song, Kehao
AU - Yi, Jingang
AU - Duan, Zhansheng
AU - Pan, Quan
AU - Huang, Panfeng
N1 - Funding Information: This work was supported in part by the National Science Foundation under CAREER award CMMI-0954966 and the National Natural Science Foundation of China under award 61403307 and the Chinese Fundamental Research Funds for the Central Universities award 3102014JCQ01070. Publisher Copyright: © 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - We present a drift-free pose estimation scheme for rigid body and its supporting platform by fusing only two gyroscopes and the relative complementary measurements. The fusion design not only provides robust relative attitude estimation between the rigid body and the platform, but also is capable of identifying partial global absolute attitudes without capturing any absolute attitude information. The pose estimation is built on a special design of the coupled kinematic model with the relative measurements between the rigid body and its supporting platform. We compare the fusion design with an alternative kinematic model and the posterior Cramer-Rao bound analyses are presented to show the completely different estimation performances. An extended Kalman filter (EKF) implementation of the fusion design is presented for the bicycle riding application.
AB - We present a drift-free pose estimation scheme for rigid body and its supporting platform by fusing only two gyroscopes and the relative complementary measurements. The fusion design not only provides robust relative attitude estimation between the rigid body and the platform, but also is capable of identifying partial global absolute attitudes without capturing any absolute attitude information. The pose estimation is built on a special design of the coupled kinematic model with the relative measurements between the rigid body and its supporting platform. We compare the fusion design with an alternative kinematic model and the posterior Cramer-Rao bound analyses are presented to show the completely different estimation performances. An extended Kalman filter (EKF) implementation of the fusion design is presented for the bicycle riding application.
UR - http://www.scopus.com/inward/record.url?scp=85006515769&partnerID=8YFLogxK
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U2 - https://doi.org/10.1109/IROS.2016.7759039
DO - https://doi.org/10.1109/IROS.2016.7759039
M3 - Conference contribution
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 90
EP - 95
BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Y2 - 9 October 2016 through 14 October 2016
ER -