PLPL-VIO: A Novel Probabilistic Line Measurement Model for Point-Line-Based Visual-Inertial Odometry

Zewen Xu, Hao Wei, Fulin Tang, Yidi Zhang, Yihong Wu, Gang Ma, Shuzhe Wu, Xin Jin

Abstract: Point and line features are complementary in Visual-Inertial Odometry (VIO) or Visual-Inertial Simultaneous Localization And Mapping (VI-SLAM) systems. The advantage of combining these two types of features relies on their proper weighting in the cost function, usually set by their uncertainty. Compared with point features, setting line segment endpoints' uncertainty with isotropic distribution is unreasonable. But the uncertainty of line feature observation, especially for the endpoints' uncertainty along the line, is difficult to set due to occlusion and fragmentation problems. In this article, we use infinite lines as the line feature observations and prove that the uncertainty of these observations is only related to the vertical uncertainty of the endpoints, thus avoiding setting the parallel uncertainty of the endpoints. Besides, we introduce a novel consistent measurement model for line features. Furthermore, for long-time constraints, we add 3D line segments into the state vector and derive how to update them properly. Finally, we construct a point-line-based VIO system that takes into account the uncertainty of line feature observations and the consistency of line feature measurements. The proposed VIO system is validated on two public datasets. The results show that the proposed method obtains the best accuracy compared with the state-of-the-art point-based VIO systems (OpenVINS, VINS-Mono), a point-line-based VIO system (PL-VINS), and a structural line-based system (StructVIO).