# Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency

arXiv: https://arxiv.org/abs/1806.09769 · HTML: https://mlsanity.com/paper/1806.09769/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Segmenting Transparent Object in the Wild with Transformer](https://mlsanity.com/paper/2101.08461/) (2021)
- [TransNet: Category-Level Transparent Object Pose Estimation](https://mlsanity.com/paper/2208.10002/) (2022)
- [Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds](https://mlsanity.com/paper/2211.16693/) (2022)
- [TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation](https://mlsanity.com/paper/2307.12400/) (2023)
- [Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion](https://mlsanity.com/paper/2103.15349/) (2021)
- [Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior](https://mlsanity.com/paper/2310.09956/) (2023)

## Abstract (arXiv)

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.