Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots

Jingyu Song, Onur Bagoren, Katherine A. Skinner

Abstract:

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