Determining binocular lenses orientation using inertial sensors: problem solution
The paper considers a binocular-shaped device, which consists of two solid bodies — lenses connected by a common axis of rotation, and introduces a solution to the problem of the absolute and relative positioning of each of the lenses of the device using accelerometers, angular velocity sensors and Hall sensors installed in each of the lenses. To solve the problem, we developed an algorithm based on the Madgwick filter. The algorithm uses data from all sensors and determines the orientation of both lenses from this data. In addition to the information received from the sensors, the solution of the problem uses information about the geometric relationship imposed on the system — the common axis of rotation of both lenses. The ARTrack video analysis system was used to verify the obtained algorithm. The results of the filter operation were verified using the records received from the video analysis system.
 Kruchinina A.P., Latonov V.V., Chertopolokhov V.A. Pilotiruemye polety v kosmos — Manned Spaceflight, 2019, no. 3, pp. 89–107.
 Roganov V.R., Filippenko V.O. Sovremennye informatsionnye tekhnologii (Modern information technologies), 2014, no. 19, pp. 162–166.
 Salychev O.S., Mkrtchyan V.I. Inzhenerny zhurnal: nauka i innovatsii — Engineering Journal: Science and Innovation, 2018, iss. 11. http://dx.doi.org/10.18698/2308-6033-2018-11-1823
 Savelev V.M., Antonov D.A. Trudy MAI (MAI Proceedings), 2011, no. 45. Available at: http://trudymai.ru/published.php?ID=25497&PAGEN_2=2
 Kaczmarek P., Tomczyґnski J., Maґnkowski T. EKF-based method for kinematic configuration estimation of finger-like structure using low grade multi-IMU system. IEEE lntemational Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016, pp. 552–557. DOI: 10.1109/MFI.2016.7849546
 Henderson E.N. An inertial measurement system for hand and finger tracking, Thesis for the degree of Master of Science in Electrical Engineering. Boston State University, 2011. Available at: https://scholarworks.boisestate.edu/td/233/
 Zihajehzadeh S., Loh D., Lee M., Hoskinson R., Park E.J. A Cascaded Two-Step Kalman Filter for Estimation of Human Body Segment Orientation Using MEMSIMU. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. Available at: https://ieeexplore.ieee.org/document/6945062
 Madgwick S.O.H. An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK), 2010, no. 25, pp.113–118.
 Kaiqiang Feng, Jie Li, Xiaoming Zhang, Chong Shen, Yu Bi, Tao Zheng, Jun Liu. A new quaternion-based Kalman filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm. Sensors, 2017. Available at: https://www.mdpi.com/1424-8220/17/9/2146/html
 Bruckner H.P., Spindeldreier C., Blume H. Modification and fixed-point analysis of a Kalman filter for orientation estimation based on 9-D inertial measurement unit data. Conf. Proc. IEEE Eng. Med. Biol. Soc. Osaka, Japan, Jul. 2013, pp. 3953–3956.
 Al Bitar N., Gavrilov A.I. Inzhenerny zhurnal: nauka i innovatsii — Engineering Journal: Science and Innovation, 2019, iss. 4. http://dx.doi.org/10.18698/2308-6033-2019-4-1870