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

TL;DR

This paper introduces a new algorithm for detecting edges and corners in unorganized point clouds, enabling improved robotic pick-and-place operations in cluttered environments by estimating object poses.

Contribution

It presents a novel edge and corner detection method that works directly on raw, noisy point cloud data and extends to 6D pose estimation for multiple objects in clutter.

Findings

Effective on raw, noisy data with minimal parameter tuning

Successfully applied to real-world robotic pick-and-place tasks

Outperforms existing methods in unorganized point cloud edge detection

Abstract

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