A Survey of Filtering based Active Localization Methods

Abstract

Localization of mobile agents is to estimate the locations of the mobile agents based on the distance and bearing measurements from neighbors or from surrounding environment, which has attracted tremendous attentions in the past several decades. Most of the traditional localization methods are passive, that the agents are passively localized based their obtained measurements. For the noises and sparsity of measurements, locating ambiguity remains a challenging problem in passive localization methods. Recent works propose active localization, which combines robot location perception and active navigation. It enables an agent to actively adjust its action based on its newest location and the newest observations, so that the agent can act properly to seek better localization, e.g. move properly to avoid ambiguity. This paper presents a survey for the state-of-the-art in filtering based active localization. We also discuss the limitations of recent active localization methods and the future research directions.