  Building vehicles capable of operating without human supervision requires the
determination of the agent's pose. Visual Odometry (VO) algorithms estimate the
egomotion using only visual changes from the input images. The most recent VO
methods implement deep-learning techniques using convolutional neural networks
(CNN) extensively, which add a substantial cost when dealing with
high-resolution images. Furthermore, in VO tasks, more input data does not mean
a better prediction; on the contrary, the architecture may filter out useless
information. Therefore, the implementation of computationally efficient and
lightweight architectures is essential. In this work, we propose the RAM-VO, an
extension of the Recurrent Attention Model (RAM) for visual odometry tasks.
RAM-VO improves the visual and temporal representation of information and
implements the Proximal Policy Optimization (PPO) algorithm to learn robust
policies. The results indicate that RAM-VO can perform regressions with six
degrees of freedom from monocular input images using approximately 3 million
parameters. In addition, experiments on the KITTI dataset demonstrate that
RAM-VO achieves competitive results using only 5.7% of the available visual
information.
