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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a novel edge and corner detection algorithm for anunorganized point cloud. Our edge detection method classifies a query point asan edge point by evaluating the distribution of local neighboring points aroundthe query point. The proposed technique has been tested on generic items suchas dragons, bunnies, and coffee cups from the Stanford 3D scanning repository.The proposed technique can be directly applied to real and unprocessed pointcloud data of random clutter of objects. To demonstrate the proposedtechnique's efficacy, we compare it to the other solutions for 3D edgeextractions in an unorganized point cloud data. We observed that the proposedmethod could handle the raw and noisy data with little variations in parameterscompared to other methods. We also extend the algorithm to estimate the 6D poseof known objects in the presence of dense clutter while handling multipleinstances of the object. The overall approach is tested for a warehouseapplication, where an actual UR5 robot manipulator is used for robotic pick andplace operations in an autonomous mode.

Edge and Corner Detection in Unorganized Point Clouds for Robotic Pick and Place Applications