A Low-Texture Robust Hybrid Feature Based Visual Odometry

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 feature-based 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-of-the-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.