Making use of what you don't see: negative information in Markov localization

This paper explores how the absence of an expected sensor reading can be used to improve Markov localization. This negative information usually is not being used in localization, because it yields less information than positive information (i.e. sensing a landmark), and a sensor often fails to detect a landmark, even if it falls within its sensing range. We address these difficulties by carefully modeling the sensor to avoid false negatives. This can also be thought of as adding an additional sensor that detects the absence of an expected landmark. We show how such modeling is done and how it is integrated into Markov localization. In real world experiments, we demonstrate that a robot is able to localize in positions where otherwise it could not and quantify our findings using the entropy of the particle distribution. Exploiting negative information leads to a greatly improved localization performance and reactivity.