AirVO: 照明変化に頑健な点・線特徴を用いた視覚オドメトリ
AirVO: An Illumination-Robust Point-Line Visual Odometry
CNNとGNNで信頼できるコーナー点を検出・マッチングし、点特徴の結果を利用して線特徴を三角測量することで、照明変化に頑健なリアルタイム視覚オドメトリを実現した。
著者: Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, Lihua Xie
分類: cs.RO
原文アブストラクト
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.
