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，如自主水下航行器（AUV）和遥控潜水器（ROV），是在冰-水界面采集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（尤其是水下航行机器人）面临的公认难题。本文提出一种紧密耦合的多传感器融合框架，通过增强对传感器故障的鲁棒性来提高定位精度。视觉图像、多普勒测速仪（DVL）、惯性测量单元（IMU）和压力传感器被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DVL辅助的特征增强方法，以进一步提升定位性能。所提方法通过在冰冻条件下采集的实地数据集进行验证，并与另外六种不同的传感器融合方案进行了结果比较。总体而言，启用关键帧和DVL辅助特征增强的方案取得了最佳性能，其均方根误差小于2米（相较于总行进距离约200米的真实轨迹）。