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Sensors 2017, 17(10), 2411; https://doi.org/10.3390/s17102411

An Improved Method of Pose Estimation for Lighthouse Base Station Extension

1
School of Optoelectronics, Beijing Institute of Technology (BIT) No. 5 Yard, Zhongguancun South Street Haidian District, Beijing 100081, China
2
Institute of Software Chinese Academy of Sciences, No. 4 South Fourth Street, Zhongguancun, Haidian District, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Received: 26 August 2017 / Revised: 8 October 2017 / Accepted: 18 October 2017 / Published: 22 October 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal. View Full-Text
Keywords: pose estimation; base station extension; indoor positioning; Lighthouse; infrared sensor; virtual reality (VR) pose estimation; base station extension; indoor positioning; Lighthouse; infrared sensor; virtual reality (VR)
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Yang, Y.; Weng, D.; Li, D.; Xun, H. An Improved Method of Pose Estimation for Lighthouse Base Station Extension. Sensors 2017, 17, 2411.

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