The most important issue for a mobile robot is orientation. Success in localization is one of the four main needs in orientation, which include: perception, localization, recognition and movement control. How to provide an accurate localization solution for mobile robot More
The most important issue for a mobile robot is orientation. Success in localization is one of the four main needs in orientation, which include: perception, localization, recognition and movement control. How to provide an accurate localization solution for mobile robots is essential in many IoT applications. To achieve this goal, in this article, a method based on two-part Kalman filter is proposed for localization of mobile robot. The proposed algorithm consists of two parts, the first part is statistical linear regression and the second part is a Kalman filter with state error vector. The proposed method is tested in comparison with the new hybrid TLNF/UK method on circular, rectangular and z-shaped motion paths that are accompanied by noise. The experimental results show that the proposed method has been able to achieve better localization accuracy and it is also observed that the estimation errors in the proposed method are less and it has been able to increase the estimation accuracy compared to the combined TLNF/UK method.
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