Probabilistic Pose Estimation of Deformable Linear Objects

This paper presents a probabilistic framework for online tracking of nodes along deformable linear objects. The proposed framework does not require an a-priori model; instead, a Bayesian Committee Machine, starting as a tabula rasa, accumulates knowledge over time. The key benefits of this approach are a lack of reliance upon extensive pre-training data, which can be difficult to obtain in sufficiently large quantities, and the ability for robust estimation of nodes subject to occlusion. Another benefit is that the uncertainties obtained during inference from the underlying Gaussian Processes can be beneficial towards subsequent handling tasks. Comparisons of the non-time series framework were conducted against conventional regression models to measure the efficacy of the proposed framework.