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AHRS Sensor Data Correction for Improved Immersion in VR

VR의 몰입감 향상을 위한 AHRS 센서 데이터 값 보정

  • Oh, Am-Suk (Department of Digital Media Engineering, Tongmyong University)
  • Received : 2018.09.27
  • Accepted : 2018.10.20
  • Published : 2018.11.30

Abstract

The VR / AR market has grown significantly due to the development of virtual reality and augmented reality in the core technology field of the 4th Industrial Revolution. Since VR is basically focused on space and time, and the human brain is very sensitive to temporal events, it is important to make accurate I / O interface technology, one of the virtual reality technologies, not to affect the brain's cognitive ability. VR depends on the technology of the hardware such as the display and the sensor for biometric signal recognition. In this paper, in order to prevent the sensitive brain from affecting the sensor device in consideration of hardware dependency of VR, it is necessary to make various corrections to lower the motion to photon (MTP) to 20m / s or less experiments on the method and filtering were carried out.

4차 산업혁명의 핵심 기술 분야의 가상현실(Virtual Reality)과 증강현실(Augmented Reality)의 발달로 인해 VR/AR시장이 크게 성장하였다. VR은 기본적으로 공간과 시간에 초점을 맞추고 있고, 인간의 두뇌는 시간적 사건에 매우 민감하기 때문에 두뇌의 인지능력에 영향을 주지 않기 위해선 가상현실 기술 중 하나인 입출력 인터페이스 기술을 정확하게 만드는 것이 중요하다. VR은 디스플레이와 생체신호인식을 위한 센서 등 하드웨어의 기술의존도가 상당하다. 본 논문에서는 VR의 하드웨어 의존도를 고려해 센서 디바이스를 이용하여 민감한 두뇌에 영향을 끼치지 않기 위해 사용자 움직임이 디스플레이 화면에 완전히 반영되는 시간 MTP(Motion to Photon)을 20m/s 이하로 낮추기 위한 여러 가지 보정방법과 필터링에 대한 실험을 진행하였다.

Keywords

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Fig. 1 Full hardware configuration

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Fig. 2 Arduino NANO

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Fig. 3 Arduino NANO Schematic

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Fig. 4 Arduino Nano Configuration Diagram

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Fig. 5 MPU-9250 Sensor

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Fig. 6 Information of axes in three-dimensional space

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Fig. 7 Process of sensor data

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Fig. 8 Circular process of step-by-step formula of multidimensional Kalman filter

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Fig. 9 Data measurement of acceleration sensor

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Fig. 10 Data measurement and drift phenomenon of geomagnetic sensor

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Fig. 11 Measure the data through sensor angle and complementary filter

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Fig. 12 Comparison of Complementary and Kalman Filter Data

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Fig. 13 Comparison of processing time of complement ary filter and Kalman filter

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