Improved Nonlinear Finite-Memory Estimation Approach for Mobile Robot Localization

In this article, we present a new mobile robot localization algorithm. The Kalman filter (KF) and particle filter (PF), which are widely used in localization problems, may show poor performance or the divergence phenomenon due to the existence of disturbances or missing measurements. This article proposes an improved nonlinear finite-memory estimation (INFME) algorithm to overcome the performance degradation problem caused by linearization errors in existing finite-memory (FM) estimation methods. To ensure robustness against noise and disturbances, the INFME algorithm was designed with an FM structure based on the minimization of an objective function, which induces reduction of adverse effects of disturbances including the linearization error. It showed superior accurate, robust, real-time performance in real mobile robot localization experiments. The accuracy and robustness of the new algorithm were verified using harsh experimental scenarios including a kidnapped robot problem and a situation in which multiple missing measurements occurred.

Automated summary

This article presents a new mobile robot localization algorithm that overcomes the performance degradation problem caused by linearization errors. The algorithm, based on an objective function minimization approach, shows superior accurate, robust, real-time performance in real mobile robot localization experiments.