TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle

Jingyu Song, Onur Bagoren, Advaith Sethuraman, Katherine A. Skinner

Abstract:

Significant work has been done on advancing localization and mapping in underwater environments. Still, state of-the-art methods are challenged by low-texture environments,
which is common for underwater settings. This makes it difficult
to use existing methods in diverse, real-world scenes. In this
paper, we present TURTLMap, a novel solution that focuses
on textureless underwater environments through a real-time
localization and mapping method. We show that this method
is low-cost, and capable of tracking the robot accurately, while
constructing a dense map of a low-textured environment in realtime. We evaluate the proposed method using real-world data
collected in an indoor water tank with a motion capture system
and ground truth reference map. Qualitative and quantitative
results validate the proposed system achieves accurate and
robust localization and precise dense mapping, even when
subject to wave conditions. The project page for TURTLMap is https://umfieldrobotics.github.io/TURTLMap.