AD-VO：基于注意视差图的尺度弹性视觉里程计
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
摘要：
视觉里程计是SLAM系统中定位模块的关键。然而，以前的方法需要调整系统以适应环境变化。在本文中，我们提出了一种基于学习的方法，帧到帧的单目视觉里程计估计。该网络仅通过视差图进行学习，不仅覆盖了环境变化，而且解决了尺度问题。此外，注意力块和跳跃排序方案的引入，以实现在各种驾驶环境下的鲁棒性能。我们的网络进行了比较，与传统的方法，使用共同的领域，如颜色或光流。实验结果表明，该网络表现出更好的性能比其他方法更高，更稳定的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
