While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue. More specifically, tasks like tying a knot, wiring a connector or even surgical suturing deal with the domain of Deformable Linear Objects (DLOs). In particular the detection of a DLO is a non-trivial problem especially under clutter and occlusions (as well as self-occlusions). The pose estimation of a DLO results into the identification of its parameters related to a designed model, e.g. a basis spline. It follows that the stand-alone segmentation of a DLO might not be sufficient to conduct a full manipulation task. This is why we propose a novel framework able to perform both a semantic segmentation and b-spline modeling of multiple deformable linear objects simultaneously without strict requirements about environment (i.e. the background). The core algorithm is based on biased random walks over the Region Adiacency Graph built on a superpixel oversegmentation of the source image. The algorithm is initialized by a Convolutional Neural Networks that detects the DLO’s endcaps. An open source implementation of the proposed approach is also provided to easy the reproduction of the whole detection pipeline along with a novel cables dataset in order to encourage further experiments.
Although the literature concerning DLOs is more focused on manipulation than on perception (see e.g. @cite_1 ), we may refer to the broader topic of to highlight related works as well as suitable alternatives to evaluate our proposal comparatively. As described in @cite_29 , the aforementioned topic pertains several kinds of objects, as summarized in table:competitors . Our target objects are , which, however, share similar properties in terms of and with the other categories highlighted in bold in table:competitors ,
@cite_29 @cite_1
2467997623 2037201547
Curvilinear object segmentation is a paramount step for many applications ranging from medical to aerial image processing. In particular, vessel segmentation in retinal images, detection of spiculated lesions in mammograms or extraction of airways in CT scans provide essential information to experts to evaluate, diagnose and propose a treatment. The significance of these applications has conducted important efforts to propose curvilinear object segmentation algorithms based on the most different techniques. The main objective of this review is to clearly present the similarities and differences between curvilinear structures in different applications and the different techniques used to segment them more effectively. To do so, we propose a general definition of curvilinear structures that encompasses the distinct models considered in the literature. In addition, we analyse and classify the mathematical techniques used to segment the curvilinear structures found across all considered applications, studying their strengths and weaknesses. In particular, we present the most relevant benchmarks related to curvilinear object segmentation as well as the best algorithms according to several performance measures. By doing so, it is acquired a wider point of view to extend the results from some fields to others, and to understand under which conditions some methodologies should be favoured over the rest of them. HighlightsWe study what curvilinear objects are found across different imagery techniques.We study and classify which algorithms have been used to segment them.We present multiple performance measures of such segmentation algorithms.We discuss which approaches are better to segment which curvilinear structures. Research on robotic manipulation has mainly focused on manipulating rigid objects so far. However, many important application domains require manipulating deformable objects, especially deformable linear objects (DLOs), such as ropes, cables, and sutures. Such objects are far more challenging to handle, as they can exhibit a much greater diversity of behaviors, and their manipulation almost inevitably requires two robotic arms, or more, performing well-coordinated motions. This paper describes a new motion planner for manipulating DLOs and tying knots (both self-knots and knots around simple static objects) using two cooperating robotic arms. This planner blends new ideas with preexisting concepts and techniques from knot theory, robot motion planning, and computational modeling. Unlike in traditional motion planning problems, the goal to be achieved by the planner is a topological state of the world, rather than a geometric one. To search for a manipulation path, the planner constructs a topologically biased probabilistic roadmap in the configuration space of the DLO. During roadmap construction, it uses inverse kinematics to determine the successive robot configurations implied by the DLO configurations and tests their feasibility. Also, inspired by the real life, the planner uses static ldquoneedlesrdquo (by analogy to the needles used in knitting) for maintaining the stability of the DLO during manipulation and to make the resulting manipulation plan robust to imperfections in the physical model of the DLO. The implemented planner has been tested both in graphic simulation and on a dual-PUMA-560 hardware platform to achieve various knots, like bowline, neck-tie, bow (shoe-lace), and stun-sail.