# Filtering Organized 3D Point Clouds for Bin Picking Applications

## TL;DR

This paper introduces a new method to filter 3D point clouds for robotic bin-picking tasks, improving performance in cluttered scenes.

## Contribution

A novel filtering technique tailored for organized 3D point clouds in bin-picking applications is proposed.

## Key findings

- The new filtering method outperforms generic outlier removal on heavily contaminated datasets.
- It was tested on six different bin datasets and showed improved filtering efficacy.

## Abstract

In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.