Learning to Predict Stability of Visual Features

Haihua Zou1; Xubin Lin1; Li He2; Yisheng Guan1

10.1109/CCDC55256.2022.10033807

2022

1948-9439

978-1-6654-7897-7

2022 34th Chinese Control and Decision Conference (CCDC)

5545-5550

15-17 Aug. 2022

Hefei, China

Visual SLAM usually stores many features and descriptors in the mapping phase. In the case of long-term visual SLAM, however, a large amount of the features in the map are unstable. In this paper, we propose a new method to predict the cross-seasonal stability of visual features. Our method is easy to incorporate with the mapping phase and retains those visual features with high repeatability, an essential property in the face of irregular environmental changes. We train the network and conduct experiments on a long-term outdoor data set. The experimental results show that our method can maintain a good prediction in different environments.

Features Long-term Stability

其他

IEEE

1.School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Haihua Zou,Xubin Lin,Li He,et al. Learning to Predict Stability of Visual Features[C],2022:5545-5550.