With the rising demand of underground construction, intelligent tunneling techniques have been increasingly studied to improve the safety and efficiency of construction. The self-positioning technology of tunneling machines is the cornerstone of intelligent tunneling, which is particularly challenging \textcolor{blue}{due to} the extreme environments of the underground tunnels. In this paper, a novel robust and real-time six degrees of freedom (6-DoF) pose estimation strategy is proposed for tunneling machines based on the computer vision and deep learning methods. A monocular camera is attached to the tunneling machine, and employed to capture the images of the artificial feature object that is set far behind the tunneling machine. A novel multi-scale variational autoencoder aided convolutional neural network (MSVAE-CNN) model is developed to estimate the current absolute 6-DoF pose of the tunneling machine in an end-to-end manner using a single monocular image, in which the multi-task variational learning scheme is able to enhance the generalization and robustness of the model and the multi-scale structure can improve the learning ability of the neural network. In our numerical experiments, a Motion Capture System is utilized to assist the acquisition of training dataset. The experimental results demonstrate the efficacy of the proposed MSVAE-CNN based pose estimation method.