In recent years, the progress made in deep learning for semantic segmentation has advanced development of semantic visual odometry (VO). Along with point-based and direct methods, VO has recently used edge features. However, mismatches are common in scenes in which the distribution of edges is complex owing to the lack of appropriate descriptors for edges at the present. In this paper, we propose a semantic-segmentation-aided edge-based VO (DSEVO). It is intended to improve the localization accuracy by decreasing mismatches in the edge alignment. In the reprojection process, the semantic and edge distance residual are considered to reduce the mismatches of edges between different frames. Then, camera motion estimation is accomplished by jointly minimizing the semantic and edge cost function. Our proposed method was evaluated on the public VKITTI and TUM RGB-D datasets. It was compared with state-of-the-art methods, including the respective feature-point-based, direct, and edge-based methods. We implemented a semantic-edge-based VO system. The experimental results showed that our method achieved the highest accuracy on most of the testing sequences.

近年来，语义分割深度学习的进步推动了语义视觉里程计（VO）的发展。除了基于点的方法和直接方法，VO 最近还使用了边缘特征。然而，由于目前缺乏适当的边缘描述符，在边缘分布复杂的场景中，不匹配是很常见的。在本文中，我们提出了一种语义分割辅助的基于边缘的 VO（DSEVO）。它旨在通过减少边缘对齐中的失配来提高定位精度。在重投影过程中，考虑了语义和边缘距离残差，以减少不同帧之间边缘的不匹配。然后，通过联合最小化语义和边缘成本函数来完成相机运动估计。我们提出的方法在公共 VKITTI 和 TUM RGB-D 数据集上进行了评估。将其与最先进的方法进行了比较，包括各自的基于特征点、直接和基于边缘的方法。我们实现了一个基于语义边缘的 VO 系统。实验结果表明，我们的方法在大多数测试序列上达到了最高的准确率。