3D Object Detection Based on Point Cloud Data

Abstract

In the Industrial robotic, computer vision is an important part of the system. The popular object used in the industrial field is a 3D pipe. The problem that is currently being developed is how to detect an object. This research aims to estimate the object detection that is, in this case, is a 3D pipe in various lighting conditions. The camera used in this research is Time of Flight. The methods applied are Remove NaN data for Pre-processing, Random Sample Consensus (RANSAC) for Segmentation, Euclidean Distance for Clustering, and Viewpoint Feature Histogram (VFH) for the object detection. A study conducted on five different objects found that the system could detect each one with a success rate of 100% for the first object, 98.05 percent for the second object, 93.97 percent for the third object, 94 percent for the fourth object, and 99.48 percent for the fifth object. Overall, the system's accuracy in detecting the object is 97.1 percent when four different lighting conditions are applied to five different objects in total.