However, SLAM, as with other non-linear least-squares problems, generally does not have any closed-form solutions [25]. Therefore, solving the problem typically requires an algorithm that starts with an initial value, either randomly selected, guessed or heuristics-based, and iteratively minimizes the cost function until convergence. Some popular standard solvers are the Gradient Descent (GD), Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms.
