Aiming at the problem that visual-inertial data fusion models built by using optimization methods have not fully considered the actual complexity,resulting in the inability to accurately simulate complex real-world states,the paper proposed an improved visual-inertial positioning technology by interactive multiple model algorithm:it was pointed out that compared to traditional filtering methods,due to the sparsity characteristics of Jacobian and Hessian matrices,optimization techniques could offer higher estimation accuracy and compete with filtering methods in computational efficiency;then,the interactive multiple model (IMM) algorithm was combined with optimization algorithms,utilizing the capability of IMM to effectively simulate the state of a single target under various scenarios,and dynamically assigning the credibility of inertial and visual data by model probabilities,to improve the optimization algorithm and enhance the pose estimation accuracy.Experimental results showed that compared to the oriented brief-simultaneous localization and mapping 3 (ORB-SLAM 3) algorithm,the proposed method could improve the root mean square (RMS) error performance of localization accuracy by 17％.
interactive multiple model algorithmintegrated navigationoptimizationfusion localizationvisual positioninginertial navigation
万方数据
维普
