Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.

翻译：机器人水下系统，例如自主水下航行器（AUV）和遥控无人潜水器（ROV），是收集冰水界面生物地球化学数据以推动科学进步的重要工具。然而，状态估计（即定位）是机器人系统（尤其是水下航行系统）中一个公认的难题。本文提出了一种紧耦合多传感器融合框架，以提高定位精度并增强对传感器失效的鲁棒性。该框架将视觉图像、多普勒测速仪（DVL）、惯性测量单元（IMU）和压力传感器集成到当前最先进的多状态约束卡尔曼滤波器（MSCKF）中进行状态估计。此外，本文还提出了一种新的基于关键帧的状态克隆机制和一种新的DVL辅助特征增强方法，以进一步改善定位性能。该方法通过在冰封环境下采集的实地数据集进行验证，并与6种不同的传感器融合配置进行了结果对比。总体而言，启用关键帧和DVL辅助特征增强的方案表现最佳，在总行程约200米的路径中，相对于真实轨迹的均方根误差小于2米。