Title: Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency,
Abstract: In order to fully function in human environments, robot perception will need
to account for the uncertainty caused by translucent materials. Translucency
poses several open challenges in the form of transparent objects (e.g.,
drinking glasses), refractive media (e.g., water), and diffuse partial
occlusions (e.g., objects behind stained glass panels). This paper presents
Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object
poses 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. We
present results of localizing and manipulating objects with translucent
materials and objects occluded by layers of translucency. Our PMCL
implementation uses observations from a Lytro first generation light field
camera to allow a Michigan Progress Fetch robot to perform grasping.