For underwater vehicles, robotic applications have the added difficulty of operating in highly unstructured and dynamic environments. Environmental effects impact not only the dynamics and controls of the robot but also the perception and sensing modalities. Acoustic sensors, which inherently use mechanically vibrated signals for measuring range or velocity, are particularly prone to the effects that such dynamic environments induce. This paper presents an uncertainty-aware localization and mapping framework that accounts for induced disturbances in acoustic sensing modalities for underwater robots operating near the surface in dynamic wave conditions. For the state estimation task, the uncertainty is accounted for as the added noise caused by the environmental disturbance. The mapping method uses an adaptive kernel-based method to propagate measurement and pose uncertainty into an occupancy map. Experiments are carried out in a wave tank environment to perform qualitative and quantitative evaluations of the proposed method. More details about this project can be found at https://umfieldrobotics.github.io/PUMA.github.io.

翻译：对于水下航行器而言，机器人应用面临在高度非结构化和动态环境中运行的额外挑战。环境影响不仅作用于机器人的动力学与控制，也影响其感知与传感模式。声学传感器本质上利用机械振动信号测量距离或速度，特别容易受到此类动态环境引起的效应影响。本文提出了一种不确定性感知的定位与建图框架，该框架考虑了在动态波浪条件下近水面运行的水下机器人的声学传感模式中由环境扰动引起的干扰。对于状态估计任务，不确定性被解释为由环境扰动引起的附加噪声。建图方法采用自适应核方法，将测量和位姿不确定性传播到占位栅格图中。在波浪水池环境中进行了实验，对所提方法进行了定性和定量评估。有关该项目的更多详细信息，请访问 https://umfieldrobotics.github.io/PUMA.github.io。