1. Improved Point–Line Visual–Inertial Odometry System Using Helmert Variance Component Estimation
- Author
-
Shoujian Zhang, Jingrong Wang, Bo Xu, and Yu Chen
- Subjects
0209 industrial biotechnology ,Inertial frame of reference ,Correlation coefficient ,Matching (graph theory) ,Computer science ,Science ,point and line features ,Helmert variance component estimation ,02 engineering and technology ,visual–inertial odometry ,020901 industrial engineering & automation ,Odometry ,line feature matching method ,0202 electrical engineering, electronic engineering, information engineering ,Point (geometry) ,Computer vision ,correlation coefficient ,business.industry ,Process (computing) ,Line (geometry) ,General Earth and Planetary Sciences ,Variance components ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Mobile platform visual image sequence inevitably has large areas with various types of weak textures, which affect the acquisition of accurate pose in the subsequent platform moving process. The visual–inertial odometry (VIO) with point features and line features as visual information shows a good performance in weak texture environments, which can solve these problems to a certain extent. However, the extraction and matching of line features are time consuming, and reasonable weights between the point and line features are hard to estimate, which makes it difficult to accurately track the pose of the platform in real time. In order to overcome the deficiency, an improved effective point–line visual–inertial odometry system is proposed in this paper, which makes use of geometric information of line features and combines with pixel correlation coefficient to match the line features. Furthermore, this system uses the Helmert variance component estimation method to adjust weights between point features and line features. Comprehensive experimental results on the two datasets of EuRoc MAV and PennCOSYVIO demonstrate that the point–line visual–inertial odometry system developed in this paper achieved significant improvements in both localization accuracy and efficiency compared with several state-of-the-art VIO systems.
- Published
- 2020