Multi-radio data fusion for indoor localization using WiFi and bluetooth

2020 ◽  
Author(s):  
Afaz Ahmed
Keyword(s):  
Data Fusion ◽  
Indoor Localization ◽  
Radio Data

An EKF-based multiple data fusion for mobile robot indoor localization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guangbing Zhou ◽  
Jing Luo ◽  
Shugong Xu ◽  
Shunqing Zhang ◽  
Shige Meng ◽  
...  
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Indoor Localization ◽  
Robot Navigation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Content Type ◽  
Multiple Data

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

scholarly journals Data Fusion of UWB and IMU Based on Unscented Kalman Filter for Indoor Localization of Quadrotor UAV

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64971-64981 ◽  
Author(s):  
Weide You ◽  
Fanbiao Li ◽  
Liqing Liao ◽  
Meili Huang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Quadrotor Uav

Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion

2017 ◽  
Vol 22 (6) ◽  
pp. 2588-2599 ◽  
Author(s):  
Payam Nazemzadeh ◽  
Daniele Fontanelli ◽  
David Macii ◽  
Luigi Palopoli
Keyword(s):  
Data Fusion ◽  
Mobile Robots ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Qr Code

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

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 344 ◽  
Author(s):  
Hui Zhang ◽  
Zonghua Zhang ◽  
Nan Gao ◽  
Yanjun Xiao ◽  
Zhaozong Meng ◽  
...  
Keyword(s):  
Data Fusion ◽  
Motion Analysis ◽  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Cost Effective ◽  
Position Estimation ◽  
Measurement Unit ◽  
Wearable Sensing ◽  
Time Motion ◽  
Sensor Module

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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