In the task of pose measurement based on monocular vision, traditional convolutional neural networks suffer from the problem that the measurement accuracy reduces dramatically in both fuzzy background and complicated background, a deep learning model based on capsule network and Bayesian network is proposed, further a pose measurement method is proposed based on moving robot and monocular vision. First of all, the new Capsules Network is adopted to locating the important joint points of monocular vision objectives; then, a simple learning algorithm for Bayesian networks is designed, and the attitudes of joint points are inferred by Bayesian networks. Finally, validation experiments are carried on complicated human pose measurement datasets, the experimental results show that the proposed method realizes a good measurement accuracy, it remains a high-level accuracy in complicated background too, the result F1-measure values equal 0.9 and 0.78 for indoors and outdoors scenarios respectively.