Multi-sensor based real-time 6-DoF pose tracking for wearable augmented reality

Fang, Wei; Zheng, Lianyu; Wu, Xiangyong

doi:10.1016/j.compind.2017.06.002

Cited by 23 publications

(2 citation statements)

References 22 publications

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“…Therefore, the term tracking is an important element in VR/AR applications, which is conducted by calculating the real‐time orientation and location of users. [ 113 ] Gesture and gait recognition is fundamental for tracking techniques. The viewer position changes can be reflected well in the VR/AR‐rendered graphics.…”

Section: Textile Electronic Devices For Vr/armentioning

confidence: 99%

Textile Electronics for VR/AR Applications

Liu

Bao

et al. 2020

Adv Funct Materials

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Textile electronics addresses fibers or fiber assemblies with electronic functions to generate, transmit, modulate, and detect electrons. Interactive textile electronic devices may provide suitable platforms for virtual reality (VR)/augmented reality (AR) applications because of their excellent performance and unique immersive features such as lightweight, handiness, flexibility, comfort, and low strain even under high deformations. This paper presents a systematic review of the literature on the state‐of‐the‐art of interactive devices, fabrication technologies, system integration, promising applications, and challenges involved in textile‐based VR/AR systems.

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Section: Textile Electronic Devices For Vr/armentioning

confidence: 99%

Textile Electronics for VR/AR Applications

Liu

Bao

et al. 2020

Adv Funct Materials

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“…In the beginning, these systems usually use camera as only sensors [146], [147]. In mobile devices, to improve accuracy and obtain absolute scale, the devices fuse data from other sensors such as IMU [148]- [151].…”

Section: Application Of Visual Odometry In Complicated and Emerging A...mentioning

confidence: 99%

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Wang,

Ma,

Chen

et al. 2020

Preprint

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Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometrybased methods, deep learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep learning-based VO (Deep VO). Therefore, this paper aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented reality and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.

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