Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency
This addresses robot perception uncertainty in human environments with translucent materials, but it is incremental as it builds on existing localization techniques.
The paper tackles the problem of robot object localization under translucent materials by proposing Plenoptic Monte Carlo Localization (PMCL), which uses a Depth Likelihood Volume descriptor and Monte Carlo methods to enable a Fetch robot to successfully grasp objects with translucent layers.
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.