LAUSR

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 due to the extreme environments of the underground tunnels. In this article, 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 multiscale 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 multitask variational learning scheme is able to enhance the generalization and robustness of the model and the multiscale 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.