A motion tracking method by combining the IMU and camera in mobile devices

Fang, Wei; Zheng, Lianyu; Deng, Huanjun

doi:10.1109/icsenst.2016.7796235

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Analysis of the

F_{d}

exponents reveal a repetitive and sparse structure [40]. With

Q_{c}

being the noise covariance matrix

Q_{c} = d i a g false(σ_{n_{a}}^{2} \cdot I, σ_{n_{b_{a}}}^{2} \cdot I, σ_{n_{ω}}^{2} \cdot I, σ_{n_{b_{ω}}}^{2} \cdot I false)

, the covariance matrix

Q_{d}

is obtained by the discretization of

Q_{c}

Q_{d} = true \int_{Δ t} F_{d} (τ) G_{c} Q_{c} G_{C}^{T} F_{d} {false(τ false)}^{T} d τ

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

Fang

Zheng

Deng³

2017

Sensors

Self Cite

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In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time.

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“…Analysis of the

F_{d}

exponents reveal a repetitive and sparse structure [40]. With

Q_{c}

being the noise covariance matrix

Q_{c} = d i a g false(σ_{n_{a}}^{2} \cdot I, σ_{n_{b_{a}}}^{2} \cdot I, σ_{n_{ω}}^{2} \cdot I, σ_{n_{b_{ω}}}^{2} \cdot I false)

, the covariance matrix

Q_{d}

is obtained by the discretization of

Q_{c}

Q_{d} = true \int_{Δ t} F_{d} (τ) G_{c} Q_{c} G_{C}^{T} F_{d} {false(τ false)}^{T} d τ

…”

Section: Methodsmentioning

confidence: 99%

“…Analysis of the

exponents reveal a repetitive and sparse structure [ 40 ]. With

being the noise covariance matrix

, the covariance matrix

is obtained by the discretization of

…”

Section: Methodsmentioning

confidence: 99%

Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

Fang

Zheng

Deng³

2017

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus it is adopted for attitude representation in state vector. Besides, the rotation matrix is also used in this paper, which can be transformed by the quaternion with the following equation [34]:boldC=(2q02−1)I3−2q0[qv]×+2qvqvT, where [·]× is the skew symmetric operator, q0 and qv=false[q1,q2,q3false]T are the real and imaginary part of the rotation quaternion, respectively.…”

Section: System Modelmentioning

confidence: 99%

An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots

Jianfeng

Sun

et al. 2019

Sensors

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The key to successful positioning of autonomous mobile robots in complicated indoor environments lies in the strong anti-interference of the positioning system and accurate measurements from sensors. Inertial navigation systems (INS) are widely used for indoor mobile robots because they are not susceptible to external interferences and work properly, but the positioning errors may be accumulated over time. Thus ultra wideband (UWB) is usually adopted to compensate the accumulated errors due to its high ranging precision. Unfortunately, UWB is easily affected by the multipath effects and non-line-of-sight (NLOS) factor in complex indoor environments, which may degrade the positioning performance. To solve above problems, this paper proposes an effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots, in which our modeling approach is simple to implement and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed. Due to the favorable property (i.e., self-adaptive adjustment) of SHFAF, the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly. Moreover, outliers can be detected and corrected by the proposed sliding window estimation with fading coefficients. This facilitates the positioning performance improvement for indoor mobile robots. The benefits of what we propose are illustrated by not only simulations but more importantly experimental results.

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“…Finally, the proposed motion is measured using the method of the follow-up experiments. [9] In this work, unbalance measurements of the markers attached to the body to control the movement of the human body based on the novel online. And for this reason, we have attached markers, including location, use of a kinematic model of the human body.…”

Section: Review Of Literaturementioning

confidence: 99%

A review of motion tracking algorithms

2018

IJLTET

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A motion tracking method by combining the IMU and camera in mobile devices

Cited by 6 publications

References 18 publications

Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots

A review of motion tracking algorithms

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