When navigating and interacting in challenging environments where sensory information is imperfect and incomplete, robots must make decisions that account for these shortcomings. We propose a novel method for quantifying and representing such perceptual uncertainty in 3D reconstruction through occupancy uncertainty estimation. We develop a framework to incorporate it into grasp selection for autonomous manipulation in underwater environments. Instead of treating each measurement equally when deciding which location to grasp from, we present a framework that propagates uncertainty inherent in the multi-view reconstruction process into the grasp selection. We evaluate our method with both simulated and the real world data, showing that by accounting for uncertainty, the grasp selection becomes robust against partial and noisy measurements. Code will be made available at https://onurbagoren.github.io/PUGS/

Underwater vehicle manipulator systems (UVMS) are increasingly popular platforms for performing subsea operations that require precision manipulation. While there is high demand for fully autonomous or even semi-autonomous systems, most UVMS still require human support teams. Developing new hardware and algorithms for autonomous underwater manipulation is challenging. Simulations do not capture the full complexity of the underwater environment, and deploying a UVMS at sea for testing/validation is resource-intensive and expensive. In this paper, we present a physical testbed for underwater manipulation that bridges the gap between simulation and full field trials. The underWater Arm-Vehicle Emulator (WAVE) is a 10-degree of freedom system designed to replicate an inspection-class UVMS. WAVE includes an underwater perception sensor and has 2 operating modes: rigid or passive-mode. In passive-mode, the ROV body can pitch similar to how a dynamically-coupled underactuated UVMS without pitch control would rotate during manipulation tasks. To validate the overall design and passive pitch concept, we evaluated the testbed during underwater experiments in energetic conditions at a wave basin. To support continued research and development in underwater robotics, we make the design open-access and freely available to the community.

Subsea hydrothermal vents, typically existing at water depths below natural light penetration, contain diverse and unique macrofaunal environments. Traditionally, long-term ecological observation has been difficult as the extreme depth, temperature and pressure make in situ video surveys challenging. However, the introduction of subsea cabled arrays has allowed for the long time series collection of high definition imagery from these vents. To study the benthic hydrothermal vent environment, we propose an inference pipeline consisting of a U-Net followed by VGG-16 CNN to perform instance segmentation of scale worms, a specific macrofaunal family. The developed pipeline exhibits an average precision (AP) of 0.671 AP@[0.5], despite the difficult camouflaged imagery and low training data inputs. We further explore full pipeline training data requirements, as the dynamic scene in question requires the pipeline to be re-trained on an approximately monthly basis for effective segmentation. We find that the VGG-16 CNN portion of the pipeline is typically more sensitive to training data variation than the U-Net portion.

This paper describes Smartpig, an algorithm for the iterative mosaicking of images of a planar surface using a unique parameterization which decomposes inter-image projective warps into camera intrinsics, fronto-parallel projections, and inter-image similarities. The constraints resulting from the inter-image alignments within an image set are stored in an undirected graph structure allowing efficient optimization of image projections on the plane. Camera pose is also directly recoverable from the graph, making Smartpig a feasible solution to the problem of simultaneous location and mapping (SLAM). Smartpig is demonstrated on a set of 144 high resolution aerial images and evaluated with a number of metrics against ground control.