English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Object pose estimation is a crucial prerequisite for robots to performautonomous manipulation in clutter. Real-world bin-picking settings such aswarehouses present additional challenges, e.g., new objects are addedconstantly. Most of the existing object pose estimation methods assume that 3Dmodels of the objects is available beforehand. We present a pipeline thatrequires minimal human intervention and circumvents the reliance on theavailability of 3D models by a fast data acquisition method and a syntheticdata generation procedure. This work builds on previous work on semanticsegmentation of cluttered bin-picking scenes to isolate individual objects inclutter. An additional network is trained on synthetic scenes to estimateobject poses from a cropped object-centered encoding extracted from thesegmentation results. The proposed method is evaluated on a syntheticvalidation dataset and cluttered real-world scenes.

Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks