CAREER: Toward Robust Multi-Vehicle Multi-Scalar Underwater Robotic Navigation - A Career Development Plan

这个职业发展计划的目标是研究新的概率方法，用于同时定位和制图（SLAM），这将提高海洋科学中机器人制图的精度和规模。 所提出的方法将扩展到multipleheterogeneous车辆，允许在长时间和多个空间尺度上进行扩展探索，并对水下环境的挑战性限制具有鲁棒性。许多陆/空SLAM方法在很大程度上不适用于水下，因为（a）缺乏非结构化海底的点特征，以及（B）与陆地、空中和空间相比，电磁、光学和声学辐射的快速衰减。 所提出的多车辆多标量SLAM导航框架通过融合来自三种不同技术的信息来克服这些挑战：1）基于实时视觉的海底导航，2）惯性导航系统，以及3）基于声学调制解调器的通信，以创建一个灵活的导航框架，该框架允许节点间测距和异构节点之间的数据共享。 通过利用感知和localizationcapability的相邻车辆通过（低带宽）distributedestimation框架，所提出的方法将提供合作导航框架，将产生改进的精度和规模的水下机器人海洋科学测绘。

The objective of this career development plan is to investigate newprobabilistic methods for simultaneous localization and mapping (SLAM)that will improve the precision and scale of robotic mapping in oceanscience. The proposed methodology will scale to multipleheterogeneous vehicles, allow for extended exploration over long timedurations and over multiple spatial scales, and be robust to thechallenging limitations of the underwater environment. Many land/airSLAM methods are largely inapplicable underwater because of (a) a lackof point features of unstructured seafloor, and (b) the rapidattenuation of electromagnetic, optical, and acoustic radiation incomparison to land, air, and space. The proposed multi-vehiclemulti-scalar SLAM navigation framework overcomes these challenges byfusing information from three disparate technologies: 1) real-timevision-based seafloor navigation, 2) inertial navigation systems, and3) acoustic modem-based communication, to create a flexible navigationframework that allows for inter-nodal ranging and data sharing amongheterogeneous nodes. By leveraging the perception and localizationcapability of neighboring vehicles via a (low-bandwidth) distributedestimation framework, the proposed methodology will provide acooperative navigation framework that will yield improved precisionand scale in underwater robotic mapping for ocean science.