We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular visual odometry methods, our approach is established on the intuition that features contribute discriminately to different motion patterns. Specifically, we propose a dual-branch recurrent network to learn the rotation and translation separately by leveraging current Convolutional Neural Network (CNN) for feature representation and Recurrent Neural Network (RNN) for image sequence reasoning. To enhance the ability of feature selection, we further introduce an effective context-aware guidance mechanism to force each branch to distill related information for specific motion pattern explicitly. Experiments demonstrate that on the prevalent KITTI and ICL_NUIM benchmarks, our method outperforms current state-of-the-art model- and learning-based methods for both decoupled and joint camera pose recovery.

本文提出了一种基于深度卷积递归神经网络的端到端视觉里程计体系结构，该体系结构使用了有指导的特征选择方法。实验表明，在流行的KITTI和ICL_NUIM基准测试中，我们的方法在解耦和关节相机姿态恢复方面都优于当前最先进的基于模型和基于学习的方法。

深度视觉里程计引导特征选择