Deformable One-Dimensional Object Detection for Routing and Manipulation

• URL: http://arxiv.org/abs/2201.06775v1
• Date: Tue, 18 Jan 2022 07:19:17 GMT
• Title: Deformable One-Dimensional Object Detection for Routing and Manipulation
• Authors: Azarakhsh Keipour and Maryam Bandari and Stefan Schaal
• Abstract summary: This paper proposes an approach for detecting deformable one-dimensional objects which can handle crossings and occlusions. Our algorithm takes an image containing a deformable object and outputs a chain of fixed-length cylindrical segments connected with passive spherical joints. Our tests and experiments have shown that the method can correctly detect deformable one-dimensional objects in various complex conditions.
• Score: 8.860083597706502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many methods exist to model and track deformable one-dimensional objects (e.g., cables, ropes, and threads) across a stream of video frames. However, these methods depend on the existence of some initial conditions. To the best of our knowledge, the topic of detection methods that can extract those initial conditions in non-trivial situations has hardly been addressed. The lack of detection methods limits the use of the tracking methods in real-world applications and is a bottleneck for fully autonomous applications that work with these objects. This paper proposes an approach for detecting deformable one-dimensional objects which can handle crossings and occlusions. It can be used for tasks such as routing and manipulation and automatically provides the initialization required by the tracking methods. Our algorithm takes an image containing a deformable object and outputs a chain of fixed-length cylindrical segments connected with passive spherical joints. The chain follows the natural behavior of the deformable object and fills the gaps and occlusions in the original image. Our tests and experiments have shown that the method can correctly detect deformable one-dimensional objects in various complex conditions.

Related papers

• Realtime Robust Shape Estimation of Deformable Linear Object [5.63550507123537]
  We propose a robust method to estimate the shape of linear deformable objects in realtime using scattered and unordered key points. We demonstrate the robustness of the method when key points are partially occluded. The proposed method is also integrated into a simulation in Unity for tracking the shape of a cable with a length of 1m and a radius of 5mm.
  arXiv  Detail & Related papers  (2024-03-24T13:36:23Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• Linear Object Detection in Document Images using Multiple Object Tracking [58.720142291102135]
  Linear objects convey substantial information about document structure. Many approaches can recover some vector representation, but only one closed-source technique introduced in 1994. We propose a framework for accurate instance segmentation of linear objects in document images using Multiple Object Tracking.
  arXiv  Detail & Related papers  (2023-05-26T14:22:03Z)
• Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies [29.998917158604694]
  We present a reinforcement learning framework that learns the interactive grasping of various geometrically distinct real-world objects. Videos of learned interactive policies are available at https://maltemosbach.org/io/geometry_aware_grasping_policies.
  arXiv  Detail & Related papers  (2022-11-20T11:47:33Z)
• Detection and Physical Interaction with Deformable Linear Objects [10.707804359932604]
  Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. There have already been successful methods to model and track deformable linear objects. We present our work on using the method for tasks such as routing and manipulation with the ground and aerial robots.
  arXiv  Detail & Related papers  (2022-05-17T01:17:21Z)
• A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation [59.29922697476789]
  We propose a novel methodology for extracting state information from image sequences via a technique to represent the state of a deformable object as a distribution embedding. Our experiments confirm that we can estimate posterior distributions of physical properties, such as elasticity, friction and scale of highly deformable objects, such as cloth and ropes.
  arXiv  Detail & Related papers  (2021-12-09T17:50:54Z)
• Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference [78.41932738265345]
  This paper proposes a plug detector that can accurately detect the objects of novel categories without fine-tuning process. We introduce two explicit inferences into the localization process to reduce its dependence on annotated data. It shows a significant lead in both efficiency, precision, and recall under varied evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-26T03:09:57Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints [11.247580943940916]
  We build high-fidelity physics simulation to aid in tracking a deformable object. We focus on the object based on RGBD images and geometric motion estimates. Our contributions allow us to outperform previous methods by a large margin in terms of accuracy.
  arXiv  Detail & Related papers  (2020-11-01T21:13:18Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.