A Scan Matching Approach to SLAM with a Dynamic Likelihood Field

This paper presents a fast scan matching approach to online SLAM supported by a dynamic likelihood field. The dynamic 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 the online SLAM and it has a low computational complexity. Scan matching is formulated as a non-linear least squares problem and solved by the Gauss-Newton method. Furthermore, to reduce the influences of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using an objective benchmark designed to compare SLAM solutions and its execution times were also analyzed. It shows to be a fast and accurate online SLAM approach, suitable for real-time operation.

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