  Visual-inertial SLAM is crucial in various fields, such as aerial vehicles,
industrial robots, and autonomous driving. The fusion of camera and inertial
measurement unit (IMU) makes up for the shortcomings of a signal sensor, which
significantly improves the accuracy and robustness of localization in
challenging environments. This article presents PLE-SLAM, an accurate and
real-time visual-inertial SLAM algorithm based on point-line features and
efficient IMU initialization. First, we use parallel computing methods to
extract features and compute descriptors to ensure real-time performance.
Adjacent short line segments are merged into long line segments, and isolated
short line segments are directly deleted. Second, a
rotation-translation-decoupled initialization method is extended to use both
points and lines. Gyroscope bias is optimized by tightly coupling IMU
measurements and image observations. Accelerometer bias and gravity direction
are solved by an analytical method for efficiency. To improve the system's
intelligence in handling complex environments, a scheme of leveraging semantic
information and geometric constraints to eliminate dynamic features and A
solution for loop detection and closed-loop frame pose estimation using CNN and
GNN are integrated into the system. All networks are accelerated to ensure
real-time performance. The experiment results on public datasets illustrate
that PLE-SLAM is one of the state-of-the-art visual-inertial SLAM systems.
