CVF-DLO: Cross-Visual-Field Branched Deformable Linear Objects Route Estimation

The perception of deformable linear objects (DLOs) poses significant challenges in robotic manipulation. Crossovers, mergings, and bifurcations of multiple DLOs complicate the identification of individual DLO physical instances. Furthermore, DLOs are often too large to be captured by a single camera, requiring the stitching of multiple overlapping views. This letter presents CVF-DLO, a cross-visual-field route estimation framework of branched DLOs (BDLOs) laid along physical surfaces such as wire harnesses, based on images from multiple viewpoints and pose-aware cameras. CVF-DLO is applicable to various perception tasks involving DLO-like structures, such as verifying connection accuracy and route consistency in cables and pipes. We propose a DLO instance segmentation method that demonstrates superior performance in handling crossings and bifurcations. The extracted DLO paths are projected onto the designed cable-laying surfaces using the camera pose and scene model. Finally, DLO routes are retrieved by searching within the spatial path domain formed by intersecting visual fields. To validate our method on wiring harnesses and intersections, we use two public DLO datasets and introduce a new BDLO dataset to benchmark against state-of-the-art DLO instance segmentation methods. Additionally, we present a cabin wiring harness dataset to evaluate the performance of the cross-visual-field route estimation.