Open AccessUnsupervised Learning of Visual Odometry with Depth Warp Constraints
Author(s) -
Haibin Shi,
Minghao Guo,
Zhi Xu,
Yuanbin Zou
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/4/042024
Subject(s) - visual odometry , artificial intelligence , computer science , unsupervised learning , computer vision , set (abstract data type) , odometry , complement (music) , deep learning , artificial neural network , pattern recognition (psychology) , robot , mobile robot , biochemistry , chemistry , complementation , programming language , phenotype , gene
Visual Odometry (VO) is one of the important components of Visual SLAM system. Some impressive work on the end-to-end deep neural networks for 6-DoF VO has appeared. We propose two-part cascade network structure to learn depth from binocular image and to infer ego-motion from consecutive frames. We propose depth warp constraints to make the Network learning more geometrically information. A lot of experiments on KITTI data set show that our model is superior to previous unsupervised methods and has comparable results with the supervised method, verifying that such a depth warp constraints perform successfully in the unsupervised deep method which is an important complement to the geometric method.