Visual-inertial odometry (VIO) is widely studied and used in autonomous robots. This article proposes a novel tightly coupled monocular VIO system based on point-line constraints (PLC-VIO). In the front end, PLC-VIO presents a line segment extraction and merging algorithm based on the EDLines method and achieves real-time feature tracking based on the geometric constraints between feature points and lines. In the back end, PLC-VIO reconstructs new 3-D landmarks of feature lines through points on the line and optimizes the states by minimizing a cost function that combines the preintegrated inertial measurement unit (IMU) error term together with the point and line reprojection error terms in a sliding window optimization framework. A loop closure module is also integrated, which enables relocalization and drift elimination. The corresponding experimental evaluations are conducted using public datasets to validate the effectiveness and robustness of the proposed system, and the results show that PLC-VIO can achieve good performance when compared with other state-of-the-art systems and, at the same time, with no compromise to real-time performance.