scholarly journals Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 63055-63064 ◽  
Author(s):  
Wu Yan ◽  
Zhihao Xu ◽  
Xuefeng Zhou ◽  
Qianxing Su ◽  
Shuai Li ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Adaptive Threshold ◽  
Bin Picking ◽  
Object Pose Estimation

scholarly journals Data-Driven Object Pose Estimation in a Practical Bin-Picking Application

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6093
Author(s):  
Viktor Kozák ◽  
Roman Sushkov ◽  
Miroslav Kulich ◽  
Libor Přeučil
Keyword(s):  
Pose Estimation ◽  
Data Gathering ◽  
Data Driven ◽  
Fixed Position ◽  
Automatic Data ◽  
Direct Light ◽  
Data Driven Approach ◽  
Bin Picking ◽  
Object Pose Estimation ◽  
Metallic Parts

This paper addresses the problem of pose estimation from 2D images for textureless industrial metallic parts for a semistructured bin-picking task. The appearance of metallic reflective parts is highly dependent on the camera viewing direction, as well as the distribution of light on the object, making conventional vision-based methods unsuitable for the task. We propose a solution using direct light at a fixed position to the camera, mounted directly on the robot’s gripper, that allows us to take advantage of the reflective properties of the manipulated object. We propose a data-driven approach based on convolutional neural networks (CNN), without the need for a hard-coded geometry of the manipulated object. The solution was modified for an industrial application and extensively tested in a real factory. Our solution uses a cheap 2D camera and allows for a semi-automatic data-gathering process on-site.

scholarly journals Object Pose Estimation for Robot Loading in Accommodation Space Using Alpha-Shape Algorithm

2021 ◽  
Author(s):  
Qingda Guo ◽  
Lulu Tang ◽  
Jianchi Zhang
Keyword(s):  
Pose Estimation ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Alpha Shape ◽  
Accommodation Space ◽  
Bin Picking ◽  
Object Pose Estimation ◽  
The Common ◽  
Setting Parameters

Abstract Robots with visual sensors have been used in various goods logistics, such as bin picking or uploading. However, there are more and more demands for the automatic blanking and loading, it is necessary to solve the problem of object pose estimation in changing accommodation space. This paper proposes a method for pose estimation in the accommodation space using alpha-shape algorithm and improved Fruit fly Optimization Algorithm (FOA). The alpha-shape volume variety of object and measured space is set to the objective function and the pose variety of object is set to six variables of improved FOA. The experiments were performed by setting parameters of improved FOA and considering the four space types represented the common accommodation shapes. Compared with previous work using convex hull, the new study using alpha-shape algorithm not only keeps the object in the accommodation space, but also maintains the object pose is at the bottom of the space and can meet the practical requirement of object placement by robot arms.

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

scholarly journals Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking

2021 ◽  
Author(s):  
Timon Hofer ◽  
Faranak Shamsafar ◽  
Nuri Benbarka ◽  
Andreas Zell
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Bin Picking
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