# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 1 Introduction  
Robotic bin-picking is a common task in manufacturing [1]. Its objective is to pick a single part from an unstructured pile in a box, as shown in Figure 1, and make the part available for the next step of the automated process [2]. Figure 1: Example of depth map for bin-picking. Marked by colored ellipse are some examples of outliers (bright pixels indicating points with unusually small depth, i.e., high z). High levels of autonomy and easily reconfigurable work cells are needed to achieve a smooth flow of the production lines. This is often achieved using 3D machine vision systems that are integrated with the robots. A schematic configuration of a typical setup, with a 3D camera and robot in fixed locations in a world coordinate frame, is shown in Figure 2. Vision systems acquire and process 3D data to provide the robot controller with spatial awareness for path planning. Planning includes the selection of a single part from a pile of parts and the design of a safe path from the starting gripper pose to the grasp pose for the selected part. Poor quality of the acquired data and erroneous results provided by a vision system may severely degrade the performance of a robot. Spurious, unfiltered 3D points provided by a vision system can be interpreted by the obstacle avoidance function as a real object and may degrade the robot's path planning algorithm. Similarly, spurious 3D points will cause an incorrectly determined six-degree-of-freedom (6 DOF) pose of the selected single part in a bin, which will likely lead to an unsuccessful attempt to grip this part by a robot [3]. The complexity of the bin-picking process varies greatly from application to application, which affects the appropriate type of machine vision hardware and processing algorithm for a particular scenario [4]. For example, if a bin contains quasi-2D objects, a single camera and an algorithm based on well-known 2D image processing techniques may be sufficient [5]. Similarly, if parts in a bin are lightweight and a vacuum gripper can be used, less accurate segmentation of a single part and its derived 6 DOF pose may still be acceptable. This may not be the case for parts that cannot be considered as quasi-2D (i.e., parts where all three dimensions are comparable) as they require more precise grasping by a gripper [6]. Another factor that impacts the complexity of bin-picking is the finish of the part's surface. For example, shiny objects are known to yield poor-quality data, and these types of objects are commonly picked from assembly lines in industrial applications [7]. Thus, enhancing the quality of the data acquired and processed by a vision system is important. Different types of machine vision systems have been integrated with robotic arms for different bin-picking scenarios [8; 9; 10; 11; 12; 13; 14; 15]. The choice of a particular system determines how the acquired data needs to be addressed by the post-processing algorithms. Systems based on laser scanning or structured light (commonly used on automated production lines) are known to output 3D point clouds, which are contaminated by spurious points floating in small regions with no correspondence to the real, physical objects. Such points are outliers that need to be removed from a dataset before further processing. Portions of these outliers can be easily identified and removed by simple thresholding (depth or z  
Figure 2: Schematic drawing of a typical setup for robotic bin-picking applications. coordinate), as shown in Figure 3a. However, the remaining parts of the 3D point cloud may still contain unfiltered outliers, which may spoil the segmentation of individual parts, as shown in Figure 3b. This paper presents a new method of filtering these difficult-to-identify outliers from the organized 3D point cloud acquired for bin-picking. The method identifies groups of 3D points with \(z\) components separated from the surrounding points by a distance \(\Delta d\).