Improved iSAM Based on Flexible Re-Linearization Threshold and Error Learning Model for AUV in Large Scale Areas

This paper proposed an improved incremental smoothing and mapping (iSAM) which combines flexible re-linearization threshold with error learning model to improve the efficiency and accuracy of navigation for autonomous underwater vehicle (AUV). The flexible threshold of the proposed method, which can avoid periodic re-linearization of iSAM, is controlled by adaptive threshold. Through the use of flexible re-linearization, the proposed method can reduce the running time and carry out re-linearization timely. Simultaneously, for the first time, the proposed method takes advantages of the fast training time of hidden-layer neural networks and Gaussian Process Regression which is more suitable for non-linearity to get error learning model for iSAM. The proposed method can achieve better accuracy with only a rough model and can be easily transplanted to other systems without cumbersome calculations of the precise model. Our algorithm has been demonstrated by a range of simulated and real datasets, especially in practical application it outperforms current available iSAM algorithms in efficiency and accuracy. The RMSE of proposed method increases by 21.7% and efficiency also has been improved by 56% than iSAM2 in practical application for AUV.