This paper deals with how the absence of an expected sensor reading can be used to improve Markov localization. Negative information has not been used for robot localization for various reasons like sensor imperfections, and occlusions that make it hard to determine if a missing sensor reading is really caused by the absence of a feature. We address these difficulties by carefully modeling the robot's main sensor, its camera. Taking into account the viewing frustum and detected obstacles, the absence of a sensor reading can be associated with the absence of that particular feature. This information can then be integrated into the localization process. We show the positive effect on robot localization in various experiments. (a) In a specific setup, the robot is able to localize using negative information where without it, it is unable to localize. (b) We demonstrate the importance of modeling occlusions and the impact of false negatives on localization. (c) We show the positive impact in a typical run
['Jan Hoffmann', 'Michael Spranger', 'Daniel Göhring', 'Matthias Jüngel', 'Hans-Dieter Burkhard']
Further studies on the use of negative information in mobile robot localization