He Wang, Qi Zhang, Zhiwen Zheng, Xiaoli Li, Ru Li, "A Low-Texture Robust Hybrid Feature Based Visual Odometry", IROS 2024
Abstract:
In low-texture scenes, Visual Odometry (VO) algorithms
often encounter challenges stemming from sparse feature
sets and reduced accuracy in feature matching. To overcome
this, integrating plane features and vanishing point characteristics
can provide additional constraints for refining camera
poses. Optical flow-based tracking methods may also offer
improved matching precision compared to traditional featurebased
approaches. Motivated by these challenges, we present
a robust Visual Odometry system tailored for low-texture
environments. Our system combines a vanishing point-based
approach for camera pose optimization with a Manhattan-aided
algorithm for matching line segments using optical flow. By
incorporating planes and vanishing points as supplementary
features for pose estimation, we enhance overall accuracy without
significant time overhead.We utilize detected line features to
compute vanishing points, improving accuracy without compromising
efficiency. In addition, our Manhattan-aided optical flow
technique supplements and refines the results of line feature
matching, further enhancing the accuracy of vanishing points.
Evaluation on various public datasets demonstrates the superior
accuracy and robustness of our system compared to state-ofthe-
art Simultaneous Localization And Mapping (SLAM) and
VO methods. Notably, our method effectively addresses issues
of failure in low-texture scenes and improves the accuracy of
line feature matching compared to baseline methods. We will
release our source code upon paper acceptance.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Science and Technology Cooperation and Exchange Special Project of ShanXi Province under Grant 202204041101016, in part by the 1331 Engineering Project of Shanxi Province - NA
Grant Reference no. : 215541015