Sonar map construction for ubiquitous robotic system using a footprint association filter

This paper presents a method for building a probability grid map for autonomous mobile robots with ultrasonic sensors using a footprint association filter (FAF). The method is based on evaluating the possibility that the acquired sonar data are all reflected by the same object. The FAF is able to associate data points with each other. Data affected by specular reflection are not likely to be associated with the same object, so they are excluded from the data cluster by the FAF, thereby improving the reliability of the data used for the probability grid map. Since the corrupted data are not used to update the probability map, it is possible to build a good quality grid map even in a specular environment. The FAF was applied to the Bayesian probability models, which are typical models used to build grid maps, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a ubiquitous home environment.