Tightly Coupled Integration of INS and UWB Using Fixed-Lag Extended UFIR Smoothing for Quadrotor Localization

Xu, Yuan; Shmaliy, Yuriy S.; Ahn, Choon Ki; Shen, Tao; Zhuang, Yuan

doi:10.1109/jiot.2020.3015351

Cited by 59 publications

(39 citation statements)

References 43 publications

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“…Shi et al [ 33 ] used commercial IMU and UWB ranging to calculate anchor coordinates which simplified the deployment of the UWB system, after which the UWB measurements and inertial measurements were fused by a tightly-coupled error-state Kalman filter. Xu et al [ 34 ] proposed a fix-lag extended finite impulse response smoother (FEFIRS) to implement UWB and INS data tight fusion, and the results showed that FEFIRS had higher robustness and accuracy compared with traditional Kalman-based schemes.…”

Section: Related Workmentioning

confidence: 99%

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Long

Kong

Zhang

et al. 2021

Sensors

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A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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Section: Related Workmentioning

confidence: 99%

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Long

Kong

Zhang

et al. 2021

Sensors

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“…It is in the stage of rapid development with challenges and opportunities. Various technologies emerge with their competing and unique characters, such as WiFi [1], visible-light [2], UWB [3], WSN [37], [38], image [39], etc. For other new technologies, please refer to [40].…”

Section: Introductionmentioning

confidence: 99%

A Robust Visual Localization Method With Unknown Focal Length Camera

Yin

Tan

et al. 2021

IEEE Access

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PnP problem is well researched in many fields, such as computer vision. It is considered the fundamental method to solve the key problems of robot SLAM. However, in pedestrian visual localization, uncalibrated PnP (UPnP), specifically PnP with unknown focal length (PnPf) is more suitable for solving the problem. Recently, a few researchers proposed some methods to alleviate this problem. However, the localization accuracy of the existing methods is not satisfied when image pixel noise is larger. In other words, RANSAC should be running before solving the PnPf problem to get less noisy input, which means the localization delay increases inevitably. In this paper, we propose a more robust method for solving the PnPf problem without the help of RANSAC based on Gröbner basis and convex optimization. We build a Gröbner basis solver on the offline stage with one instance in the prime field. Then, we substitute the coefficients with real value and find multiple solutions on the online stage. Finally, we construct a convex optimization program to seek the final robust solution to PnPf problem. The other purpose of this paper is to provide a second-level localization experience for the end-user. The simulation result shows that our method can give a localization solution, which is more reliable than benchmark methods by both synthetic and real data verified.

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“…Nowadays, the quadrotor has been widely used in many fields (Xu et al, 2020a;Nguyen & Hong, 2019;Kou et al, 2018). Consequently, many approaches have been proposed for the quadrotor (Liang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

Shen

et al. 2021

PeerJ Computer Science

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Due to some harsh indoor environments, the signal of the ultra wide band (UWB) may be lost, which makes the data fusion filter can not work. For overcoming this problem, the neural network (NN) assisted Kalman filter (KF) for fusing the UWB and the inertial navigation system (INS) data seamlessly is present in this work. In this approach, when the UWB data is available, both the UWB and the INS are able to provide the position information of the quadrotor, and thus, the KF is used to provide the localization information by the fusion of position difference between the INS and the UWB, meanwhile, the KF can provide the estimation of the INS position error, which is able to assist the NN to build the mapping between the state vector and the measurement vector off-line. The NN can estimate the KF’s measurement when the UWB data is unavailable. For confirming the effectiveness of the proposed method, one real test has been done. The test’s results demonstrate that the proposed NN assisted KF is effective to the fusion of INS and UWB data seamlessly, which shows obvious improvement of localization accuracy. Compared with the LS-SVM assisted KF, the proposed NN assisted KF is able to reduce the localization error by about 54.34%.

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Tightly Coupled Integration of INS and UWB Using Fixed-Lag Extended UFIR Smoothing for Quadrotor Localization

Cited by 59 publications

References 43 publications

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

A Robust Visual Localization Method With Unknown Focal Length Camera

Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

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