Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Zhang, Shengkai; Wang, Wei; Jiang, Tao

doi:10.1109/tie.2020.2984457

Cited by 19 publications

(8 citation statements)

References 26 publications

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“…In this experiment, we compare Marvel with the state-ofthe-art RF-based MAV state estimation systems, CWISE [40] and WINS [41]. They use WiFi signals to estimate MAV states.…”

Section: Comparison With Other Rf-based State Estimatorsmentioning

confidence: 99%

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

Zhang¹,

Wang²,

Zhang³

et al. 2021

IEEE Trans. on Mobile Comput.

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The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99 • for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.

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“…In this experiment, we compare Marvel with the state-ofthe-art RF-based MAV state estimation systems, CWISE [40] and WINS [41]. They use WiFi signals to estimate MAV states.…”

Section: Comparison With Other Rf-based State Estimatorsmentioning

confidence: 99%

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

Zhang¹,

Wang²,

Zhang³

et al. 2021

IEEE Trans. on Mobile Comput.

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“…Wang et al [27] enables human activity recognition but needs to wear an RFID reader on the user's body. WiCapture [11], Rover [3] and WINS [8] require the user to hold an active WiFi radio to enable the WiFi sensing. Such instrumentations of users hinder these approaches from widely deployments.…”

Section: A Device-based Sensing Systemmentioning

confidence: 99%

“…Radio frequency (RF) sensing is an emerging research field and has attracted considerable attentions. It impels many remarkable applications, such as indoor localization [1], [2], [3], [4], gesture recognition [5], [6], fall detection [7], robot navigation [8], [9], [10] and human motion tracking [11], [12]. Despite the success in diverse applications, recent years have witnessed the rapid proliferation of RF-based human imaging technologies, which provide privacy-preserving services for applications like surveillance, security inspection, and health monitoring.…”

Section: Introductionmentioning

confidence: 99%

WiImage: Fine-grained Human Imaging from Commodity WiFi

Zhang

Liu

et al. 2021

Preprint

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Privacy-preserving wireless-based human imaging technologies attract much attention in academia and industry. They can serve in surveillance, security inspection, and health monitoring, while preventing the privacy leakage from current camera-based surveillance system. However, previous solutions either requires dedicated hardware or costly infrastructure deployment, which hurts their practicality. In this paper, we propose WiImage, a low-cost, instantly-deployed sensing system that can capture a fine-grained human image and infer his contour using ubiquitous WiFi. WiImage is free of pre-training or placing any marker on a user’s body. WiImage consists of two technical components. First, a lightweight multipath resolution algorithm exploit the spatial and temporal correlation of WiFi packets to address the multipath and extract reflected signals from a human body. Second, an imaging algorithm infers the contour of a human from WiFi reflections. We prototype WiImage using offthe-shelf WiFi chips and conduct experiments in several typical indoor settings. The results show that WiImage can recover a representative human figure and use it to identify the user precisely.

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“…Thus, extra sensors are necessary to strengthen indoor positioning. In the past decades, WiFi [2][3][4], Bluetooth [5][6][7], ultra-wideband (UWB) [8][9][10], and microelectro-mechanical system (MEMS) [11][12][13] have been studied for indoor positioning. Among these techniques, MEMS sensors are more competitive as their independence of the existing infrastructures in indoor environments [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Geng

Xia

2021

Micromachines

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The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

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Wi-Fi-Inertial Indoor Pose Estimation for Microaerial Vehicles

Cited by 19 publications

References 26 publications

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

WiImage: Fine-grained Human Imaging from Commodity WiFi

Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

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