Latent Representation Self-Supervised Pose Network for Accurate Monocular Pipe Pose Estimation

Abstract

Accurate pipe pose estimation plays a pivotal role in the automatic assembly of pipelines. Recently, data-driven deep neural networks have been proven capable of estimating pose. Nonetheless, a large number of labeled datasets are required during the training process. One effective solution is to estimate pose using self-supervised learning. However, existing algorithms are difficult to deal with textureless objects (like pipes), and they avoid the occlusion problem. To this end, we propose a latent representation self-supervised pose network (LSPN) for accurate monocular pipe pose estimation. We train our network with synthetic RGB data, where only a few labeled samples are used to establish the latent pose space, while a large number of structured unlabeled samples are used to learn latent pose representation in self-supervised learning. Experiments demonstrate that LSPN achieves excellent performance on real data and is robust to different environments, such as illumination changes and self-occlusion.