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Efficient localization based on scan matching with a continuous likelihood field

This paper presents a fast scan matching approach to mobile robot localization supported by a continuous likelihood field. The likelihood field plays a central role in the approach, as it avoids the necessity to establish direct correspondences; it is the connection link between scan matching and robotic localization, and it provides a reduced computational complexity. Scan matching is formulated as a non-linear least squares problem and solved by the Gauss-Newton and Levenberg-Marquardt methods. Furthermore, to reduce the influences of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using a publicly available dataset and compared with AMCL, a state-of-the-art localization algorithm. Our proposal shows to be a fast and accurate localization algorithm suitable for any type of operation.