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Accurate and robust object state estimation enables successful objectmanipulation. Visual sensing is widely used to estimate object poses. However,in a cluttered scene or in a tight workspace, the robot's end-effector oftenoccludes the object from the visual sensor. The robot then loses visualfeedback and must fall back on open-loop execution.  In this paper, we integrate both tactile and visual input using a frameworkfor solving the SLAM problem, incremental smoothing and mapping (iSAM), toprovide a fast and flexible solution. Visual sensing provides global poseinformation but is noisy in general, whereas contact sensing is local, but itsmeasurements are more accurate relative to the end-effector. By combining them,we aim to exploit their advantages and overcome their limitations. We explorethe technique in the context of a pusher-slider system. We adapt iSAM'smeasurement cost and motion cost to the pushing scenario, and use aninstrumented setup to evaluate the estimation quality with different objectshapes, on different surface materials, and under different contact modes.

Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation