Bias reduction based on maximum likelihood estimates with application in scan-based localization

In this paper, a novel bias reduction method is proposed to analytically express and reduce the bias arising in localization problems, thereby improving the localization accuracy. The proposed bias reduction method mixes Taylor series and a maximum likelihood estimate, and leads to an easily calculated analytical bias expression in terms of a known maximum likelihood cost function. In the simulations we apply the proposed method to the scan-based localization problem. Monte Carlo simulation results demonstrate the performance of the proposed method in this context.