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In order to fully function in human environments, robot perception will needto account for the uncertainty caused by translucent materials. Translucencyposes several open challenges in the form of transparent objects (e.g.,drinking glasses), refractive media (e.g., water), and diffuse partialocclusions (e.g., objects behind stained glass panels). This paper presentsPlenoptic Monte Carlo Localization (PMCL) as a method for localizing objectposes in the presence of translucency using plenoptic (light-field)observations. We propose a new depth descriptor, the Depth Likelihood Volume(DLV), and its use within a Monte Carlo object localization algorithm. Wepresent results of localizing and manipulating objects with translucentmaterials and objects occluded by layers of translucency. Our PMCLimplementation uses observations from a Lytro first generation light fieldcamera to allow a Michigan Progress Fetch robot to perform grasping.

Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency