Observation‐driven Bayesian Filtering for Global Location Estimation in the Field Area

Global localization has long been considered one of the most important but also one of the most challenging localization problems for mobile robots. Current studies of global localization in the literature are mainly based on the Bayesian filtering technique, which can provide an elegant statistical framework for uncertainty management and multisensory fusion. However, the majority of implementations of Bayesian filters for global localization obey the same update rules in such a location‐driven sense that they guess the robot location first and then adjust the guess by incorporating the current observation data. This leads to some problematic consequences in that the system suffers from great computational load in a large application area and it cannot recover from localization failure. To overcome the above limitations, this paper deviates from the conventional update rules of Bayes filters and proposes a new approach: the observation‐driven Bayes filters (OD‐BFs). As the name implies, OD‐BFs estimate the robot state just according to the most recent observations and then adjust the estimate by incorporating the dead‐reckoning information. We further implement an observation‐driven Bayes filter to globally estimate the robot pose in the field area. This global localization system features an effective pose estimation framework that can operate with a large amount of point data in a coarse‐to‐fine manner. Sufficient experiments were carried out to determine both the advantages and the disadvantages of our OD‐BF localization approaches compared with previous ones.