A Non-Linear Least Squares Approach to SLAM using 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: 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 that allows us to solve it using Gauss-Newton or Levenberg-Marquardt methods. Furthermore, to reduce the influence of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using an objective benchmark designed to compare different SLAM solutions. Additionally, the execution times of our proposal were also analyzed. The obtained results show that the proposed approach provides a fast and accurate online SLAM, suitable for real-time operation.