An Improved Monocular PL-SlAM Method with Point-Line Feature Fusion under Low-Texture Environment

Abstract

Traditional visual SLAM only relies on the point features in the scene to complete positioning and mapping. When the texture information in the scene is missing, it affects the accuracy of pose estimation and mapping. In the artificial structured environment, there are a lot of structured lines that can be utilized. Compared with point features, line features contain richer information. For example, structure lines can be used to construct surface features. To improve the robustness and stability of visual SLAM positioning in a low-texture environment, we propose a new point-line feature Visual inertial navigation system based on traditional SLAM method, which makes full use of the structural line features in the scene. Compared to the traditional SLAM system which use point-line features, we adopt a new point-line feature error reprojection model-cross-product of between projection line feature and detected line feature and nonlinear optimization strategy under long line, aiming to increase the robustness in a low-texture environment. The proposed algorithm has been verified by EuRoc dataset and real-world scenarios, and the results show that our algorithm has a greater improvement in accuracy.