6 accepted papers
In this work, we address the problem of real-time dense depth estimation from monocular images for mobile underwater vehicles. We formulate a deep learning model that fuses sparse depth measurements from triangulated features to improve the depth predictions and solve the problem of scale ambiguity.…
A known challenge for computer vision methods applied to the underwater domain is that nonlinear attenuation of light in underwater environments distorts the color signal in captured imagery, resulting in inconsistent color and contrast at varying distances to an imaged target. While surface reflect…
Autonomous underwater gliders (AUGs) are capable of traversing basin scale distances but lack sufficient localization accuracy to operate without periodic surfacing to obtain GPS fixes and constrain localization drift. Conventionally, AUGs use a dynamic flight model with depth averaged current corre…
This letter presents a novel visual feature based scene mapping method for underwater vehicle manipulator systems (UVMSs), with specific emphasis on robust mapping in natural seafloor environments. Our method uses GPU accelerated SIFT features in a graph optimization framework to build a feature map
There has been much recent interest in deep learning methods for monocular image based object pose estimation. While object pose estimation is an important problem for autonomous robot interaction with the physical world, and the application space for monocular-based methods is expansive, there has
Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environ