Research on UAV refusal positioning technology based on multisensor fusion

A multi-sensor fusion SLAM algorithm based on visual-assisted loop closure detection is proposed to address issues such as lighting variations and viewpoint changes caused by collisions in high-speed UAV scenarios. To enhance the robustness of the algorithm in complex environments, the front-end visual subsystem employs an ORB feature point detector for visual odometry, addressing the challenges of the visual subsystem under complex lighting conditions. Additionally, image loop closure verification is added to the scan context loop closure detection method, and factor graph optimization is used to correct trajectory vector drift, solving the problem of cumulative errors in odometry loop closure scenarios. The algorithm was validated on the KITTI dataset, and experimental results show that it outperforms other classical algorithms in terms of stability, robustness, and accuracy.