Abstract
The employment of linear features to enhance the positioning precision and robustness of point-based VIO (visual-inertial odometry) has attracted mounting attention, especially for UAV (unmanned aerial vehicle) applications where reliable 6-DoF pose estimation is critical for autonomous navigation, mission execution, and safety. This paper presents GPLVINS—GNSS (global navigation satellite system)-point-line-visual-inertial navigation system—a UAV-tailored enhancement of the nonlinear optimization-based GVINS (GNSS-visual-inertial navigation system). Unlike GVINS, which struggles with feature extraction in weak-texture environments and depends entirely on point features, GPLVINS innovatively integrates line features into its state optimization framework to enhance robustness and accuracy. While existing studies adopt the LSD (line segment detector) algorithm for line feature extraction, this approach often generates numerous short line segments in real-world scenes. Such an outcome not only increases computational costs but also degrades pose estimation performance. In order to address this issue, the present study proposes an NMS (non-maximum suppression) strategy for the refinement of LSD. The line reprojection residual is then formulated as the distance between point and line, which is incorporated into the nonlinear optimization process. Experimental validations on open-source datasets and self-collected UAV datasets across indoor, outdoor, and indoor–outdoor transition scenarios demonstrate that GPLVINS exhibits superior positioning performance and enhanced robustness for UAVs in environments with feature degradation or drastic lighting intensity variations.