SM/VIO：在基于模型的里程计与视觉惯性里程计之间切换的鲁棒水下状态估计
SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry
AI总结：
针对水下视觉惯性里程计易因环境因素失效的问题，提出SM/VIO框架，通过运动学模型与本体感知传感器在VIO失效时维持位姿估计并结合健康监测实现及时切换，经Aqua2实验验证了其鲁棒性。
摘要：
本文探讨了水下作业中视觉惯性状态估计的鲁棒性问题。在恶劣环境中运行的水下机器人需要始终获知其位姿。由于能见度差、颜色丢失和缺乏特征，所有基于视觉的定位方案都容易失效。所提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VIO的轨迹与模型驱动里程计的轨迹整合为一个连贯集合，始终保持一致的位姿。健康监测跟踪VIO过程，确保在两个估计器之间及时切换。最后，在整体轨迹上实现了回环检测。由此形成的框架是一个在基于模型和视觉惯性里程计之间切换的鲁棒估计器（SM/VIO）。Aqua2潜水器的多次部署实验结果证明了该方法在珊瑚礁和沉船上的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
