  We propose to integrate text objects in man-made scenes tightly into the
visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to
treat each detected text as a planar feature which is rich of textures and
semantic meanings. The text feature is compactly represented by three
parameters and integrated into visual SLAM by adopting the
illumination-invariant photometric error. We also describe important details
involved in implementing a full pipeline of text-based visual SLAM. To our best
knowledge, this is the first visual SLAM method tightly coupled with the text
features. We tested our method in both indoor and outdoor environments. The
results show that with text features, the visual SLAM system becomes more
robust and produces much more accurate 3D text maps that could be useful for
navigation and scene understanding in robotic or augmented reality
applications.
