半透明環境下でのロボット把持のための光線空間モンテカルロ物体位置推定
Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency
光線空間（ライトフィールド）観測を用い、透明・半透明物体や半透明層に隠れた物体の姿勢を推定するDepth Likelihood Volumeとモンテカルロ位置推定法を提案し、ロボットによる把持を実現した。
著者: Zheming Zhou, Zhiqiang Sui, Odest Chadwicke Jenkins
分類: cs.RO, cs.AI
原文アブストラクト
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.
