High accuracy model-based object pose estimation for autonomous recharging applications

2016 ◽  
Author(s):  
Hanno Jaspers ◽  
Georg R. Mueller ◽  
Hans-Joachim Wuensche
Keyword(s):  
Pose Estimation ◽  
High Accuracy ◽  
Model Based ◽  
Object Pose Estimation

Model-Based 3D Object Pose Estimation Using Occluding Contours

2011 ◽  
Vol 131 (4) ◽  
pp. 505-514
Author(s):  
Kenichi Maruyama ◽  
Yoshihiro Kawai ◽  
Fumiaki Tomita
Keyword(s):  
Pose Estimation ◽  
3D Object ◽  
Occluding Contours ◽  
Model Based ◽  
Object Pose Estimation

scholarly journals Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 122 ◽  
Author(s):  
Chi-Yi Tsai ◽  
Kuang-Jui Hsu ◽  
Humaira Nisar
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Image Features ◽  
Estimation Methods ◽  
Reference Image ◽  
Practical Applications ◽  
Pose Tracking ◽  
Model Based ◽  
Object Pose Estimation ◽  
Template Tracking

Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.

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.

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