Robot self-localization by means of vision

We present an application of vision-based object recognition capabilities to the self-positioning-problem of an autonomous robot. Alphanumeric signs are placed in the robot environment as position markers and perceived through an on-board CCD camera on a pan-tilt head. Sign recognition is performed by a neural network based system, driven by some a-priori knowledge about the characteristics of the objects used as markers (signs). When given a map of the location of markers, the robot is able to estimate its position from the information extracted through perceived images. Marker distances and angular displacements allow the computation of a position uncertainty region for the mobile robot. Even using common, human readable markers, localization is performed with an average position accuracy within a few centimeters.