Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields

Xiaochuan Yin, Xiangwei Wang, Xiaoguo Du, Qijun Chen; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5870-5878

Scale recovery is one of the central problems for monocular visual odometry. Normally, road plane and camera height are specified as reference to recover the scale. The performances of these methods depend on the plane recognition and height measurement of camera. In this work, we propose a novel method to recover the scale by incorporating the depths estimated from images using deep convolutional neural fields. Our method considers the whole environmental structure as reference rather than a specified plane. The accuracy of depth estimation contributes to the scale recovery. We improve the performance of depth estimation by considering two consecutive frames and egomotion of camera into our networks. The depth refinement and scale recovery are obtained iteratively. In this way, our method can eliminate the scale drift and improve the depth estimation simultaneously. The effectiveness of our method is verified on the KITTI dataset for both visual odometry and depth estimation tasks.