Underwater visual-inertial navigation is challenging due to the poor visibility and presence of outliers in underwater environments. The navigation performance is closely related to outlier detection and elimination. Existing methods assume the inertial odometry is accurate enough for outlier detection, which is not valid for low-cost inertial applications. We propose a novel iterative smoothing and outlier detection method aiming for underwater navigation. Using the dataset collected from an underwater robot and fiducial markers, experimental results confirm that the method can successfully eliminate the outliers and enhance navigation accuracy.

翻译：水下视觉-内衣导航由于水下环境外层的可见度和存在不足而具有挑战性。导航性能与外层探测和清除密切相关。现有方法假设惯性odosaty 足够准确,足以进行外层探测,而对于低成本惯性应用来说,这是无效的。我们提出了一个新的迭代平滑和异端探测方法,以水下导航为目标。利用从水下机器人和浮标收集的数据集,实验结果证实该方法能够成功地消除外层,提高导航的准确性。