A new localization method for mobile robot by data fusion of vision sensor data and motion sensor data

2012 ◽  
Author(s):  
Tae-jae Lee ◽  
Wook Bahn ◽  
Byung-moon Jang ◽  
Ho-Jeong Song ◽  
Dong-il Dan Cho
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Sensor Data ◽  
Motion Sensor ◽  
Vision Sensor ◽  
Localization Method

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.

Fault Diagnosis and Reconfiguration for Mobile Robot Localization Based on Multi-Sensors Data Fusion

2021 ◽  
pp. 1-23
Author(s):  
Linda Hachemi ◽  
Mohamed Guiatni ◽  
Abedlkrim Nemra
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Mobile Robot ◽  
Fault Tolerant ◽  
Sensor Data ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Estimation System ◽  
And Performance ◽  
Robot Position

In this paper, we propose a new approach for fault tolerant localization using multi-sensors data fusion for a unicycle-type mobile robot. The main contribution of this paper is a new architecture proposal for fault diagnosis and reconfiguration for mobile robot localization using multi-sensors data fusion and the duplication/comparison approach. Four different sensors usually embedded in mobile robots (Camera, IMU, GPS, and Odometer) are considered, while six different sensors couples combinations are used for sensor data fusion and the duplication of the localization and estimation system. In order to reach this aim, three different filters (EKF, SVSF, and ASVSF) have been proposed and compared. For each selected filter, a comparison mechanism is then introduced to compute different residuals by comparing the estimated robot position for each sensor couples separately. Faults are then detected using the structural residual diagnosis method. This approach assumes the occurrence of a single fault at a given time. A reconfiguration mechanism is then applied by selected the healthy sensors couple and their corresponding fusion filter. Several scenarios are considered for navigation-based fault tolerant localization approaches. Simulation results are presented to illustrate the advantage and performance of the proposed architecture. The proposed solutions are implemented and validated successfully using the V-REP simulator.

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