This paper proposes an illumination-robust visual odometry (VO) system that incorporates both accelerated learning-based corner point algorithms and an extended line feature algorithm. To be robust to dynamic illumination, the proposed system employs the convolutional neural network (CNN) and graph neural network (GNN) to detect and match reliable and informative corner points. Then point feature matching results and the distribution of point and line features are utilized to match and triangulate lines. By accelerating CNN and GNN parts and optimizing the pipeline, the proposed system is able to run in real-time on low-power embedded platforms. The proposed VO was evaluated on several datasets with varying illumination conditions, and the results show that it outperforms other state-of-the-art VO systems in terms of accuracy and robustness. The open-source nature of the proposed system allows for easy implementation and customization by the research community, enabling further development and improvement of VO for various applications.

翻译：本文提出了一种光照鲁棒的视觉里程计（VO）系统，该系统融合了基于加速学习的角点算法与扩展的线特征算法。为应对动态光照变化，系统采用卷积神经网络（CNN）和图神经网络（GNN）检测并匹配可靠且信息丰富的角点，进而利用点特征匹配结果以及点线特征的分布实现对线特征的匹配与三角化。通过加速CNN与GNN模块并优化流水线，该系统能够在低功耗嵌入式平台上实现实时运行。在多种光照条件下的数据集上评估表明，所提VO系统在精度与鲁棒性方面均优于其他现有先进VO系统。系统的开源特性便于研究社区轻松实现与定制，从而推动VO在各类应用中的进一步发展与改进。