Hybrid pose measurement based on fusion of IMU and monocular vision

被引：3

作者：

Sun C. ^{[1
,2
]}

Xu H. ^{[1
]}

Zhang B. ^{[2
]}

Wang P. ^{[1
,2
]}

Guo X. ^{[1
]}

机构：

[1] State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin

[2] Science and Technology on Electro-Optic Control Laboratory, Luoyang Institute of Electro-Optic Equipment, AVIC, Luoyang

来源：

Xu, Huaiyuan (hyxu@tju.edu.cn) | 2017年 / Tianjin University卷 / 50期

关键词：

Coordinate calibration; Fusion; IMU; Pose measurement; Visual measurement;

D O I：

10.11784/tdxbz201604003

中图分类号：

学科分类号：

摘要：

Quick and accurate object-pose measurement is widely used in aerospace and robot fields.Inertial pose measurement, using Euler angle iteration formula, has an advantage in measuring velocity but suffers from slow drift.In contrast, visual pose measurement based on POSIT algorithm is slow but accurate.This paper proposes a method for measuring attitude by integrating the data from the two types of measurements. H∞ filter is used for fusion.In this paper, Euler drift error curve, caused by the drift of gyroscopic sensor, is corrected and updated according to the difference between the hybrid measurement result and the output of inertial measurement.In order to calibrate the coordinate relationships in pose measurement system, this paper promotes a double-vector orthogonal calibration method and a three-picture fast calibration method.Those methods use the output of IMU and the information from three captured pictures when the object takes a determined rotation.Experimental results show that hybrid pose measurement with fusion resolution is quicker than visual orientation measurement, with higher accuracy. © 2017, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.

引用

页码：313 / 320

页数：7

共 20 条

[1]

Murphy-Chutorian E., Trivedi M.M., Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness, IEEE Transactions on Intelligent Transportation Systems, 11, 2, pp. 300-311, (2010)

[2]

Qian S., Research on Relative Measurement and Attitude Control for On-Orbit Servicing SpaceCraft, (2010)

[3]

Foxlin E., Altshuler Y., Naimark L., Et al., Flight Tracker: A novel optical/inertial tracker for cockpit enhanced vision, Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 2004(ISMAR 2004), pp. 212-221, (2004)

[4]

Wang K., Research on High-Precision Attitude Estimation Based on INS/Vision Fusion for Indoor Flight, (2012)

[5]

Abdel-Aziz Y.I., Karara H.M., Hauck M., Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry, Photogrammetric Engineering & Remote Sensing, 81, 2, pp. 103-107, (2015)

[6]

Dementhon D.F., Davis L.S., Model-based object pose in 25 lines of code, International Journal of Computer Vision, 15, 1-2, pp. 123-141, (1995)

[7]

Chen X., Du C., Zhu X., Et al., A sensing-fusion tracking method for inversion endoscope surgery, Chinese Journal of Lasers, 41, 12, (2014)

[8]

Welch G., Bishop G., An introduction to the Kalman filter, University of North Carolina at Chapel Hill, 7, pp. 127-132, (1995)

[9]

Foxlin E., Inertial head-tracker sensor fusion by a complimentary separate-bias Kalman filter, Proceedings of the IEEE Virtual Reality Annual International Symposium, 1996, (1996)

[10]

Foxlin E.M., Generalized architecture for simultaneous localization, auto-calibration, and map-building, IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, pp. 527-533, (2002)

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