Position Tracking for Virtual Reality Using Commodity WiFi

Kotaru, Manikanta; Katti, Sachin

doi:10.1109/cvpr.2017.286

Cited by 80 publications

(12 citation statements)

References 54 publications

(48 reference statements)

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“…where the covariance matrix P b k b k+1 can be calculated using the pre-integration technique proposed in [25]. To improve the accuracy of PAoA estimation, we use a Vector Network Analyzer to conduct a one-time calibration of transceiver responses of WiFi chips [26]. We utilize Robot Operating System (ROS) as the communication middleware.…”

Section: Imu Measurement Modelmentioning

confidence: 99%

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Zhang

Wang

Jiang

2021

IEEE Trans. Ind. Electron.

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This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initializationfree for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of 61.7 cm and the attitude error of 0.92°. Index Terms-Micro aerial vehicle, pose estimation, WiFi.

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Section: Imu Measurement Modelmentioning

confidence: 99%

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Zhang

Wang

Jiang

2021

IEEE Trans. Ind. Electron.

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“…Spatial tracking performance for the DK2 HMD provides sub-millimeter translation accuracy (Kotaru & Katti, 2017) via an external camera with near-infrared CMOS sensor taking measurements at 60 Hz. Moreover, the DK2 HMD provides high rotation tracking accuracy (<0.5°) and precision (Chang, Hsu, Hsu, & Chen, 2016) via an accelerometer, gyroscope, and magnetometer embedded in the headset, taking measurements at 1000Hz.…”

Section: Head-trackingmentioning

confidence: 99%

Head jitter enhances three-dimensional motion perception

Fulvio

Miao

Rokers³

2021

Journal of Vision

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Motion perception is a critical function of the visual system. In a three-dimensional environment, multiple sensory cues carry information about an object's motion trajectory. Previous work has quantified the contribution of binocular motion cues, such as interocular velocity differences and changing disparities over time, as well as monocular motion cues, such as size and density changes. However, even when these cues are presented in concert, observers will systematically misreport the direction of motion-in-depth. Although in the majority of laboratory experiments head position is held fixed using a chin or head rest, an observer's head position is subject to involuntary small movements under real-world viewing conditions. Here, we considered the potential impact of such "head jitter" on motion-in-depth perception. We presented visual stimuli in a head-mounted virtual reality device that facilitated low latency head tracking and asked observers to judge 3D object motion. We found performance improved when we updated the visual display consistent with the small changes in head position. When we disrupted or delayed head movement-contingent updating of the visual display, the proportion of motion-in-depth misreports again increased, reflected in both a reduction in sensitivity and an increase in bias. Our findings identify a critical function of head jitter in visual motion perception, which has been obscured in most (head-fixed and non-head jitter contingent) laboratory experiments.

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“…Indoor localization is playing a crucial role in many applications including shopping navigation, security surveillance [9,10], augmented reality [11,12,20] and health monitoring [13][14][15]. In particular, RF-based localization systems become increasingly popular due to the pervasive usage of WIFI, Bluetooth Low Energy (BLE) and LoRa devices.…”

Section: Introductionmentioning

confidence: 99%

TyrLoc

Yin

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This work presents the design and implementation of TyrLoc, an accurate multi-technology switching MIMO localization system that can be deployed on low-cost SDRs. TyrLoc only uses a single RF Chain to switch on each antenna in an antenna array within the coherence time asynchronously, thus mimicking a MIMO platform to pinpoint the positions of WIFI, Bluetooth Low Energy (BLE) and LoRa devices. TyrLoc makes three key technical contributions. First, TyrLoc modifies the firmware of inexpensive PlutoSDR that controls the antenna switching pattern and tags the signal associated with each antenna. Second, it develops a two-stage fine-grained carrier frequency offset (CFO) calibration algorithm that harnesses the agile antenna switching pattern and is 10× more accurate than the baseline method. Third, TyrLoc employs an interpolated transform approach to facilitate angle-of-arrival (AoA) estimation in the presence of missing antennas. The AoA-based localization experiments in a multipath-rich indoor environment show that TyrLoc with eight antennas achieves the median errors of 63cm for WIFI, 39cm for BLE and 32cm for LoRa, respectively. CCS CONCEPTS• Networks → Location based services.

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Position Tracking for Virtual Reality Using Commodity WiFi

Cited by 80 publications

References 54 publications

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Head jitter enhances three-dimensional motion perception

TyrLoc

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