This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck.

翻译：本文针对水下作业中视觉惯性状态估计的鲁棒性问题展开研究。在复杂环境中作业的水下机器人需随时掌握自身位姿。所有基于视觉的定位方案均易因能见度差、色彩丢失及特征缺失等问题而失效。本文提出方法利用机器人运动学模型与本体感知传感器，在视觉惯性里程计（VIO）失效期间维持位姿估计。此外，将成功执行的VIO轨迹与模型驱动里程计轨迹整合为一致性集合，确保始终获得稳定的位姿。健康监测模块追踪VIO过程，确保两个估计器之间的及时切换。最后，对整体轨迹实施闭环优化。由此形成的框架实现了基于模型与视觉惯性里程计（SM/VIO）的鲁棒估计器切换。通过Aqua2水下机器人在珊瑚礁与沉船环境中的多次部署实验，验证了本方法在复杂水下场景中的鲁棒性。