论文标题
AD-VO：使用细心差异图
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
论文作者
论文摘要
视觉进程是SLAM系统中本地化模块的必不可少的关键。但是，以前的方法需要调整系统以调整环境更改。在本文中，我们提出了一种基于学习的方法，用于框架到框架单眼视觉探测器估计。所提出的网络仅通过差距图学习，不仅涵盖了环境变化，还可以解决规模问题。此外，引入了注意力障碍和跳过订购方案，以在各种驾驶环境中实现稳健的性能。将我们的网络与使用常见域（例如颜色或光流）的常规方法进行了比较。实验结果证实，所提出的网络比其他具有更高和更稳定结果的方法显示出更好的性能。
Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.
