  Coordinating proximity and tactile imaging by collocating cameras with
tactile sensors can 1) provide useful information before contact such as object
pose estimates and visually servo a robot to a target with reduced occlusion
and higher resolution compared to head-mounted or external depth cameras, 2)
simplify the contact point and pose estimation problems and help tactile
sensing avoid erroneous matches when a surface does not have significant
texture or has repetitive texture with many possible matches, and 3) use
tactile imaging to further refine contact point and object pose estimation. We
demonstrate our results with objects that have more surface texture than most
objects in standard manipulation datasets. We learn that optic flow needs to be
integrated over a substantial amount of camera travel to be useful in
predicting movement direction. Most importantly, we also learn that state of
the art vision algorithms do not do a good job localizing tactile images on
object models, unless a reasonable prior can be provided from collocated
cameras.
