Reducing Effect of Outliers in Landmark-based Spatial Localization using MLESAC

In the landmark-based localization problem, movement and ambiguity of landmarks and imperfect identification process make measurements of the landmarks completely different from its true value. The incorrect measured data have degraded existing localization methods in the practical applications. This paper proposes a framework to improve accuracy of the existing landmark-based localization methods regardless of such incorrect measured data. The framework is based on Maximum Likelihood Estimation Sample Consensus (MLESAC). It samples a set of measured data randomly to estimate position and orientation, and the estimated pose is evaluated through likelihood of whole measured data with respect to the result. Iterations of sampling, estimation, and evaluation are performed to find the best result to maximize the likelihood. Simulation results demonstrate that the proposed framework improved the the existing localization methods. Analysis using a concept of loss functions also explains that the framework is superior compared to previous researches such as Random Sample Consensus (RANSAC).