Optimal Robot Pose Estimation Using Scan Matching by Turning Function

Bahram Sadeghi Bigham (), Omid Abbaszadeh, Mazyar Zahedi-Seresht, Shahrzad Khosravi and Elham Zarezadeh
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Bahram Sadeghi Bigham: Department of Computer Science, Faculty of Mathematical Sciences, Alzahra University, Tehran 1993893973, Iran
Omid Abbaszadeh: Department of Computer Engineering, University of Zanjan, Zanjan 4537138791, Iran
Mazyar Zahedi-Seresht: Department of Quantitative Studies, University Canada West, Vancouver, BC V6Z 0E5, Canada
Shahrzad Khosravi: Department of Quantitative Studies, University Canada West, Vancouver, BC V6Z 0E5, Canada
Elham Zarezadeh: Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 4513766731, Iran

Mathematics, 2023, vol. 11, issue 6, 1-15

Abstract: The turning function is a tool in image processing that measures the difference between two polygonal shapes. We propose a localization algorithm for the optimal pose estimation of autonomous mobile robots using the scan-matching method based on the turning function algorithm. There are several methodologies aimed at moving the robots in the right way and carrying out their missions well, which involves the integration of localization and control. In the proposed method, the localization problem is implemented in the form of an optimization problem. Afterwards, the turning function algorithm and the simplex method are applied to estimate the localization and orientation of the robot. The proposed algorithm first receives the polygons extracted from two sensors’ data and then allocates a histogram to each sensor scan. This algorithm attempts to maximize the similarity of the two histograms by converting them to a unified coordinate system. In this way, the estimate of the difference between the two situations is calculated. In more detail, the main objective of this study is to provide an algorithm aimed at reducing errors in the localization and orientation of mobile robots. The simulation results indicate the great performance of this algorithm. Experimental results on simulated and real datasets show that the proposed algorithms achieve better results in terms of both position and orientation metrics.

Keywords: localization; optimization; SLAM; image processing autonomous vehicle; robot; pose estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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