Pose estimation of robotic end-effectors under low speed motion using EKF with inertial and SE(3) measurements

Xiaohan Chen, Gim Song Soh, Shaohui Foong, Kevin Otto

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

This paper is concerned with pose (position and orientation) estimation of robotic end-effectors under low speed motion where its acceleration is far less than the gravity. An Extended Kalman Filter (EKF) is designed to fuse measurements from an inertial measurement unit (IMU) and a SE(3) measurement system. The IMU consists of a tri-axis accelerometer and a tri-axis gyroscope that sense the end-effector acceleration and angular velocity. The SE(3) measurement system tracks the absolute position and orientation of the end-effector. The filtering scheme has two features. Firstly, the IMU's acceleration measurement, which is dominated by the gravity, is used as observation instead of input for better state estimation. Secondly, it efficiently merges the end-effector orientation measurement with its prediction. Experimental results show the effectiveness of the proposed filter.

Original languageEnglish
Title of host publicationAIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherIEEE
Pages1585-1590
Number of pages6
Volume2015-August
ISBN (Electronic)9781467391078
DOIs
Publication statusPublished - 25 Aug 2015
MoE publication typeA4 Article in a conference publication
EventIEEE/ASME International Conference on Advanced Intelligent Mechatronics - Busan, Korea, Republic of
Duration: 7 Jul 201511 Jul 2015

Conference

ConferenceIEEE/ASME International Conference on Advanced Intelligent Mechatronics
Abbreviated titleAIM
CountryKorea, Republic of
CityBusan
Period07/07/201511/07/2015

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