  This paper presents a novel approach to underwater terrain mapping for
Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex
3D environments. The proposed methodology creates a probabilistic elevation map
of the terrain using a monocular image learning-based scene range estimator as
a sensor. This scene range estimator can filter transient objects such as fish
and lighting variations. The mapping approach considers uncertainty in both the
estimated scene range and robot pose as the AUV moves through the environment.
The resulting elevation map can be used for reactive path planning and obstacle
avoidance to allow robotic systems to approach the underwater terrain as
closely as possible. The performance of our approach is evaluated in a
simulated underwater environment by comparing the reconstructed terrain to
ground truth reference maps, as well as demonstrated using AUV field data
collected within in a coral reef environment. The simulations and field results
show that the proposed approach is feasible for obstacle detection and range
estimation using a monocular camera in reef environments.
