  We present MidasTouch, a tactile perception system for online global
localization of a vision-based touch sensor sliding on an object surface. This
framework takes in posed tactile images over time, and outputs an evolving
distribution of sensor pose on the object's surface, without the need for
visual priors. Our key insight is to estimate local surface geometry with
tactile sensing, learn a compact representation for it, and disambiguate these
signals over a long time horizon. The backbone of MidasTouch is a Monte-Carlo
particle filter, with a measurement model based on a tactile code network
learned from tactile simulation. This network, inspired by LIDAR place
recognition, compactly summarizes local surface geometries. These generated
codes are efficiently compared against a precomputed tactile codebook
per-object, to update the pose distribution. We further release the YCB-Slide
dataset of real-world and simulated forceful sliding interactions between a
vision-based tactile sensor and standard YCB objects. While single-touch
localization can be inherently ambiguous, we can quickly localize our sensor by
traversing salient surface geometries. Project page:
https://suddhu.github.io/midastouch-tactile/
