  Underwater caves are challenging environments that are crucial for water
resource management, and for our understanding of hydro-geology and history.
Mapping underwater caves is a time-consuming, labor-intensive, and hazardous
operation. For autonomous cave mapping by underwater robots, the major
challenge lies in vision-based estimation in the complete absence of ambient
light, which results in constantly moving shadows due to the motion of the
camera-light setup. Thus, detecting and following the caveline as navigation
guidance is paramount for robots in autonomous cave mapping missions. In this
paper, we present a computationally light caveline detection model based on a
novel Vision Transformer (ViT)-based learning pipeline. We address the problem
of scarce annotated training data by a weakly supervised formulation where the
learning is reinforced through a series of noisy predictions from intermediate
sub-optimal models. We validate the utility and effectiveness of such weak
supervision for caveline detection and tracking in three different cave
locations: USA, Mexico, and Spain. Experimental results demonstrate that our
proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring
good generalization performance while offering 10+ FPS on single-board (Jetson
TX2) devices.
