Intelligent assistance positioning methodology based on modified iSAM for AUV using low-cost sensors

This paper focuses on the application of low-cost sensors for autonomous underwater vehicle (AUV). We propose an intelligent assistance positioning methodology by combining the modified incremental smoothing and mapping (iSAM) and constrained optimally pruned extreme learning machine (OP-ELM) for low-cost sensors, which makes full use of GPS data and produce a variety of correction models. Compared to relinearizing and variable reordering by period batch step in the original iSAM, modified iSAM is implemented variable reordering alone and conducted adaptive relinearization when the value of local Chi-square exceeded a certain threshold. Meanwhile, a novel constrained OP-ELM is presented by mapping the output to the constraint space, which provides full guarantee for generating reliable correction model. When GPS is valid, the constrained OP-ELM is applied to the low-cost sensors to generate correction model. Simultaneous, the correction model of measurement for modified iSAM is also given by this way. Once GPS becomes invalid, the correction models are used to amend the low-cost sensors data and measurement model for getting more accurate location information. Experimental results and analysis show that the proposed method outperforms the traditional algorithm, which RMSE can improve by at most 83.8% than Extended Kalman Filter's (EKF).