ABSTRACT

Localisation technology is one of the most important challenges of underwater vehicle applications that accomplish any scheduled mission in the complex underwater environment. Currently, the Simultaneous Localisation and Mapping (SLAM) of the Autonomous Underwater Vehicle (AUV) is becoming a hotspot research. AUVs have, only recently, received more attention and underwater platforms continue to dominate the research. To ensure the success of an accurate AUV localisation mission, the problem of drift on the estimated trajectory must be overcome. In order to improve the positioning accuracy of the AUV localisation, a new filter referred to as the Adaptive Smooth Variable Structure Filter (ASVSF) based SLAM positioning algorithm is proposed. To verify the improvement of this filter, the combined SVSF and the Extended Kalman Filter (EKF) are presented. Experimental results based on dataset for underwater SLAM algorithm show the accuracy and stability of the ASVSF AUV localisation position. Several experiments were tested under real-life conditions with an autonomous underwater vehicle based on different filters. The results of these filters have been compared based on Root Mean Squared Error (RMSE) and in terms of localisation and map building errors. It is shown that the adaptive SVSF-SLAM strategy obtains the best performance compared to other algorithms.

摘要

定位技术是在复杂水下环境中完成任何预定任务的水下航行器应用的最重要挑战之一。目前，自主水下航行器（AUV）的同时定位与建图（SLAM）正成为研究热点。直到最近，AUV 才受到更多关注，水下平台继续主导研究。为了确保准确的 AUV 定位任务的成功，必须克服估计轨迹上的漂移问题。为了提高AUV定位的定位精度，提出了一种基于自适应平滑可变结构滤波器（ASVSF）的SLAM定位算法。为了验证该滤波器的改进，提出了组合 SVSF 和扩展卡尔曼滤波器 (EKF)。基于水下SLAM算法数据集的实验结果表明了ASVSF AUV定位位置的准确性和稳定性。在真实条件下使用基于不同过滤器的自主水下航行器进行了多项实验。这些过滤器的结果已根据均方根误差 (RMSE) 以及定位和地图构建误差进行了比较。结果表明，与其他算法相比，自适应 SVSF-SLAM 策略获得了最佳性能。这些过滤器的结果已根据均方根误差 (RMSE) 以及定位和地图构建误差进行了比较。结果表明，与其他算法相比，自适应 SVSF-SLAM 策略获得了最佳性能。这些过滤器的结果已根据均方根误差 (RMSE) 以及定位和地图构建误差进行了比较。结果表明，与其他算法相比，自适应 SVSF-SLAM 策略获得了最佳性能。