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.

One of the most commonly faced tasks in industrial robots is bin picking.  Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.

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.