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.

自监督学习VO的一种特殊考虑一致性的方法，采用具有两层卷积LSTM模块的姿态网络对姿态预测中的长期依赖进行建模，通过自监督损失进行训练，包括模拟几何VO中的循环一致性丢失的循环一致性丢失，并通过引入一种损失使网络能够在训练期间超出小时间窗口。结果表明，在几个VO数据集中表现出竞争性结果，包括KITTI和TUM RGB-D。

通过自我监督长期建模学习单目视觉里程计