Publication Date: 7/21/2020
Authors: Yuliang Zou, Virginia Tech, NEC Laboratories America, Inc., Pan Ji, NEC Laboratories America, Inc., Quoc Huy Tran, NEC Laboratories America, Inc., Jia Bin Huang, Virginia Tech, Manmohan Chandraker, NEC Laboratories America, Inc., UCSD
Abstract: Monocular visual odometry (VO) suffers severely from error accumulation during frame to frame pose estimation. In this paper, we present a self supervised learning method for VO with special consideration for consistency over longer sequences. To this end, we model the long term dependency in pose prediction using a pose network that features a two layer convolutional LSTM module. We train the networks with purely self supervised losses, including a cycle consistency loss that mimics the loop closure module in geometric VO. Inspired by prior geometric systems, we allow the networks to see beyond a small temporal window during training, through a novel a loss that incorporates temporally distant (e.g., O(100)) frames. Given GPU memory constraints, we propose a stage wise training mechanism, where the first stage operates in a local time window and the second stage refines the poses with a “global” loss given the first stage features. We demonstrate competitive results on several standard VO datasets, including KITTI and TUM RGB D.
Publication Link: https://arxiv.org/pdf/2007.10983.pdf