Combined Binocular Vision and IMU Navigation Algorithm Based on Nonlinear Optimization

To address the problem that multiple interfering factors join the optimization process and lead to large errors in the optimization results, a nonlinear optimization algorithm based on the Cauchy robust kernel function is proposed to directly reduce the weight of interfering factors in the optimization solution model, and the fusion of binocular camera and IMU data is performed by the Dog-Leg algorithm. Simulation results show that the algorithm has significant advantages in localization and navigation accuracy in complex environments by comparing with ROVIO and VINS-Mono in two aspects of algorithm accuracy and algorithm performance, as well as reducing the time complexity and space complexity of the algorithm and improving the robustness of the algorithm.