Keyframe Selection with Information Occupancy Grid Model for Long-term Data Association

Weinan Chen1; Hanjing Ye2; Lei Zhu1; Chao Tang2; Changfei Fu2; Yonggang Chen3; Hong Zhang2

10.1109/IROS47612.2022.9981050

2022

2153-0858

978-1-6654-7928-8

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2786-2793

23-27 Oct. 2022

Kyoto, Japan

As the basics of Visual Simultaneous Localization And Mapping (VSLAM), keyframes play an essential role. In previous works, keyframes are selected according to a series of view change-based strategies for short-term data association (STDA). However, the texture enrichment of frames is always ignored, resulting in the failure of long-term data association (LTDA). In this paper, we propose an information enrichment selection strategy with an information occupancy grid model and a deep descriptor. Frame is expressed by a deep global descriptor for a statistical explainable abstraction, in which the texture enrichment is indicated. Based on the abstraction, an information occupancy grid model is established to measure the information enrichment and the potential LTDA ability. Evaluations on variant datasets are conducted, showing the advantage of our proposed method in terms of keyframe selection and tracking precision. Also, the statistical explainability of the deep descriptor is provided. The proposed keyframe selection strategy can improve LTDA and tracking precision, especially in situations with repeated observations and loop-closures.

VSLAM Keyframe Selection Occupancy Grid Model

其他

IEEE

1.Guangdong University of Technology, Guangzhou, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
3.DongGuan Polytechnic, Dongguan, China

Weinan Chen,Hanjing Ye,Lei Zhu,et al. Keyframe Selection with Information Occupancy Grid Model for Long-term Data Association[C],2022:2786-2793.