SLAM algorithm based on joint optimization of vision and inertia

SLAM (Simultaneous Localization and Mapping) is a key technology for mobile robots to accomplish autonomous perception tasks. However, the performance of a single sensor limits the application of different scenarios, therefore, this paper proposes a SLAM algorithm for the fusion of vision and inertia: VI Fusion (Visual Inertia Fusion), which can effectively improve the stability and accuracy of the system in complex environments. First, the improved visual features are combined with the joint initialization of IMU; second, a minimization function for the joint optimization of vision and inertia is designed; and finally, the EuRoC dataset and the real outdoor scene are selected for the comparison experiments. The results show that the algorithmic trajectory error of this paper is reduced by 55.5% on average, and the algorithmic trajectory of this paper has better consistency and continuity with the reference trajectory generated by RTK compared with similar SLAM algorithms in the outdoor scene test.