Enhanced Scan Matching Algorithm for Precise Autonomous Mobile Robot Localization

Abstract

Mobile robot localization is essential for autonomous navigation, allowing robots to accurately determine their positions within an environment. Traditional scan matching algorithms primarily only provide an area estimation of the robot's location without the exact coordinates within its environment. This paper presents a work on improving the scan matching localization algorithm using K-nearest neighbour (KNN), that is able to provide 2D coordinate information of the mobile robot position (x,y). The mobile robot with an RP lidar and high capability of a computer system is used in this study. Typical algorithms often only encompass input preparation, scan matching, and area classification stages. The proposed localization algorithm introduces a fourth stage aimed at determining the coordinates of the mobile robot within its environment. Three distance parameters (Euclidean, Mahalanobis, and Manhattan), are investigated to determine the optimal choice for KNN.  The proposed improved algorithm employing Euclidean distance achieved an accuracy of 93% when classifying 100 non-reference test samples within a range of 4cm area. Furthermore, the mobile robot continuously maintains a coordinate determination accuracy of less than 3cm in 30 samples across all local maps.  These findings hold promise for applications requiring precise mobile robot localization.