LVIF: a lightweight tightly coupled stereo-inertial SLAM with fisheye camera

Abstract

Abstract To enhance the real-time performance and reduce CPU usage in feature-based visual SLAM, this paper introduces a lightweight tightly coupled stereo-inertial SLAM with fisheye cameras, incorporating several key innovations. First, the stereo-fisheye camera is treated as two independent monocular cameras, and the SE(3) transformation is computed between them to minimize the CPU burden during stereo feature matching and eliminate the need for camera rectification. Another important innovation is the application of maximum-a-posteriori (MAP) estimation for the inertial measurement unit (IMU), which effectively reduces inertial bias and noise in a short time frame. By optimizing the system’s parameters, the constant-velocity model is replaced from the beginning, resulting in improved tracking efficiency. Furthermore, the system incorporates the inertial data in the loop closure thread. The IMU data are employed to determine the translation direction relative to world coordinates and utilized as a necessary condition for loop detection. Experimental results demonstrate that the proposed system achieves superior real-time performance and lower CPU usage compared to the majority of other SLAM systems.

Keywords

• SLAM
• State estimation
• Graph optimization
• Fisheye camera