Extended Kalman Filter based fusion of reliable sensors using fuzzy logic

Das, Tanmoy Kumar; Harischandra, P. A. Diluka; Abeykoon, A. M. Harsha S.

doi:10.1109/mercon.2017.7980456

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…At the end of this study, a comparison between ANFIS and extended Kalman filter (EKF) (16,17) was conducted to analyze the improvement of robot localization accuracy. The comparative analysis is presented in Fig.…”

Section: Resultsmentioning

confidence: 99%

Localization for Outdoor Mobile Robot Using LiDAR and RTK-GNSS/INS

Thepsit,

Konghuayrob,

Saenthon

et al. 2024

Sensors and Materials

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Two types of sensors, light detection and ranging (LiDAR) and real-time kinematic of global navigation satellite system with inertial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning. Therefore, a sensor fusion approach based on an adaptive-network-based fuzzy inference system (ANFIS) was implemented to enhance reliability. In this research, data from both sensors were collected to create a dataset for training with ANFIS. The findings indicated that the model derived from the fusion of these two sensors provided results that were much closer to the actual values obtained using each sensor independently. The result demonstrated the effectiveness of the ANFIS-based fusion method in terms of improving the accuracy and reliability of the positioning system for outdoor mobile robots.

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Section: Resultsmentioning

confidence: 99%