A Low-Texture Monocular Visual Odometer Based on Point-Line Feature

When the camera is in a low-textured scene, it is difficult for the simultaneous localization and mapping (SLAM) algorithm based on point features to track sufficient effective point features, which leads to low accuracy and weak robustness, or even results in algorithm failure. To tackle this problem, a low-texture monocular visual odometer based on point-line feature is proposed in this work. Firstly, the data association accuracy and robustness are improved by using the complementation of point-feature and line-feature. Based on this, an environmental-feature map with geometric information is constructed. We thoroughly evaluate this approach and the new initialization strategy on the TUM RGB-D benchmark. The results demonstrate that the use of lines does not only improve the original ORB-SLAM solution in poorly textured frames, but also systematically improve the solution in sequence frames combining points and lines without compromising the efficiency.

Automated summary

In this study, a low-texture monocular visual odometer based on point-line features is proposed to address the challenge of insufficient effective point features in low-textured scenes. The accuracy and robustness of data association are improved by combining point and line features, leading to the construction of an environmental-feature map with geometric information. Evaluations on the TUM RGB-D benchmark show that the use of lines not only enhances the performance in poorly textured frames but also systematically improves the solution in sequence frames combining points and lines without compromising efficiency.