Deformable One-Dimensional Object Detection for Routing and Manipulation
This addresses a critical gap for robotics and automation systems that need to manipulate objects like cables, though it is incremental as it builds on existing tracking methods by providing initialization.
The paper tackles the problem of detecting deformable one-dimensional objects like cables in images, which is a bottleneck for autonomous applications, by proposing an algorithm that outputs a chain of segments to handle crossings and occlusions, with tests showing correct detection in complex conditions.
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.