Enhanced 6D measurement by integrating an Inertial Measurement Unit (IMU) with a 6D sensor unit of a laser tracker

被引:14
作者
Yang, Linghui [1 ]
Liao, Ruiying [1 ]
Lin, Jiarui [1 ]
Sun, Bo [1 ]
Wang, Zheng [2 ]
Keogh, Patrick [2 ]
Zhu, Jigui [1 ]
机构
[1] Tianjin Univ, State Key Lab Precis Measuring Technol & Instrume, Tianjin, Peoples R China
[2] Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England
基金
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词
Laser tracker; 6D sensor; 6D measurement; IMU; Data fusion; Optical dimensional metrology; KALMAN FILTER; NAVIGATION; ENVIRONMENTS; METROLOGY; SMOOTHER; MOTION;
D O I
10.1016/j.optlaseng.2019.105902
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Six-degree-of-freedom (6D) sensors enhance the measurement capability of traditional three-degree-of-freedom (3D) laser trackers. However, the classical 6D measurement techniques still have shortcomings in actual use, such as the problem of line of sight and relatively low data acquisition rate. The proposed approach by integrating an Inertial Measurement Unit (IMU) with a 6D sensor unit of a laser tracker is effective to overcome these limitations. The error is corrected by the combination of a Kalman filter and a backward smoothing algorithm. The Kalman filter only works when the 6D sensor's data is being sent through, while the backward smoothing algorithm works during the whole process. The experiments are performed to compare the error in three positions and three rotational orientations between the proposed method and the Kalman filter and evaluate the effects of different rates and IMU frequencies on the algorithm. The simulations are also performed to estimate the maximum outage time. The results verify that the proposed method can solve the problem of line of sight and low data acquisition rate effectively.
引用
收藏
页数:11
相关论文
共 31 条
[1]   Driftless 3-D Attitude Determination and Positioning of Mobile Robots By Integration of IMU With Two RTK GPSs [J].
Aghili, Farhad ;
Salerno, Alessio .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2013, 18 (01) :21-31
[2]   Industrial Applications of the Kalman Filter: A Review [J].
Auger, Francois ;
Hilairet, Mickael ;
Guerrero, Josep M. ;
Monmasson, Eric ;
Orlowska-Kowalska, Teresa ;
Katsura, Seiichiro .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2013, 60 (12) :5458-5471
[3]   A multi-view stereo based 3D hand-held scanning system using visual-inertial navigation and structured light [J].
Ayaz, Shirazi Muhammad ;
Danish, Khan ;
Bang, Joon-Young ;
Park, Soo-In ;
Roh, YoungJun ;
Kim, Min Young .
INTERNATIONAL SYMPOSIUM OF OPTOMECHATRONICS TECHNOLOGY (ISOT 2015), 2015, 32
[4]  
Bridges R.E., 2014, U.S. Patent no, Patent No. 8848203
[5]   Kalman Filter for Robot Vision: A Survey [J].
Chen, S. Y. .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2012, 59 (11) :4409-4420
[6]   Applications of Microelectromechanical Systems in Industrial Processes and Services [J].
Dean, Robert Neal, Jr. ;
Luque, Antonio .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2009, 56 (04) :913-925
[7]   Analysis and modeling of inertial sensors using Allan variance [J].
EI-Sheimy, Naser ;
Hou, Haiying ;
Niu, Xiaoji .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2008, 57 (01) :140-149
[8]  
Gao X, 2017, VISUAL SLAM XIV THEO
[9]   An approach to benchmarking of loosely coupled low-cost navigation systems [J].
Gonzalez, R. ;
Giribet, J. I. ;
Patino, H. D. .
MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS, 2015, 21 (03) :272-287
[10]  
GORDON JA, 2015, J CMSC, V10, P12