Robust and accurate UWB‐based indoor robot localisation using integrated EKF/EFIR filtering

Xu, Yuan; Shmaliy, Yuriy S.; Ahn, Choon Ki; Tian, Guohui; Chen, Xiyuan

doi:10.1049/iet-rsn.2017.0461

Cited by 48 publications

(30 citation statements)

References 30 publications

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“…To manage the performance degradation problem of UWB for NLoS localization, Yang [40] proposed a novel NLoS mitigation method based on a sparse pseudo-input Gaussian process (SPGP) with a low complexity. To improve the accuracy of the UWB-based indoor robot localization, Xu et al [41] integrated an extended Kalman filter (EKF) and an extended unbiased finite impulse response (EFIR) filter, and the final estimate was obtained by fusing the outputs of both filters using probabilistic weights. According to the above investigations, UWB is considered a promising solution for indoor localization due to its accuracy.…”

Section: (2) Rfid-based Indoor Localizationmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

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Section: (2) Rfid-based Indoor Localizationmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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“…In their experiment, they used the parameters in line-of-sight (LOS) condition to improve estimated locations in non-line-of-sight (NLOS) condition by using a combined Gaussian and Gamma distribution. In [43], the integration of extended Kalman filter (EKF) and extended unbiased finite impulse response (EFIR) filter was used in order to have more optimized and robust results. The integration of these two filters provided both robustness and accuracy required for localization with UWB sensors in a noisy environment like a construction site.…”

Section: Uwbmentioning

confidence: 99%

Automated Data Acquisition in Construction with Remote Sensing Technologies

2020

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Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing.

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“…If the state is a vector, then the partial derivative parameters can be assembled into a new matrix, which is called a Jacobian matrix. Generally, in order to localize the vehicle's position, researchers derive the Jacobian matrix based on the transition and measurement models for handling the vehicle's noisy sensor data [25][26][27][28][29][30]. If an EKF based on a Jacobian matrix approximates a nonlinear function using a high order of Taylor series, it also works well in transforming nonlinear functions into linear ones.…”

Section: Introductionmentioning

confidence: 99%

Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter

Lin

Yoon

Kim

2020

Sensors

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Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed. In this study, a sensor fusion approach for self-driving cars was developed. To localize the vehicle position, we propose a particle-aided unscented Kalman filter (PAUKF) algorithm. The unscented Kalman filter updates the vehicle state, which includes the vehicle motion model and non-Gaussian noise affection. The particle filter provides additional updated position measurement information based on an onboard sensor and a high definition (HD) map. The simulations showed that our method achieves better precision and comparable stability in localization performance compared to previous approaches.

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Robust and accurate UWB‐based indoor robot localisation using integrated EKF/EFIR filtering

Cited by 48 publications

References 30 publications

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Automated Data Acquisition in Construction with Remote Sensing Technologies

Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter

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