59 citations · 66 across the 10 of their papers we have counts for
12 papers
IBURD: Image Blending for Underwater Robotic Detection
Jungseok Hong, Sakshi Singh, Junaed Sattar
We present an image blending pipeline, \textit{IBURD}, that creates realistic synthetic images to assist in the training of deep detectors for use on underwater autonomous vehicles…
Diver Identification Using Anthropometric Data Ratios for Underwater Multi-Human-Robot Collaboration
Jungseok Hong, Sadman Sakib Enan, Junaed Sattar
Recent advances in efficient design, perception algorithms, and computing hardware have made it possible to create improved human-robot interaction (HRI) capabilities for autonomou…
Using Monocular Vision and Human Body Priors for AUVs to Autonomously Approach Divers
Michael Fulton, Jungseok Hong, Junaed Sattar
Direct communication between humans and autonomous underwater vehicles (AUVs) is a relatively underexplored area in human-robot interaction (HRI) research, although many tasks (\eg…
ROW-SLAM: Under-Canopy Cornfield Semantic SLAM
Jiacheng Yuan, Jungseok Hong, Junaed Sattar +1
We study a semantic SLAM problem faced by a robot tasked with autonomous weeding under the corn canopy. The goal is to detect corn stalks and localize them in a global coordinate f…
Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
Jungseok Hong, Karin de Langis, Cole Wyeth +2
Mobile robots in unstructured, mapless environments must rely on an obstacle avoidance module to navigate safely. The standard avoidance techniques estimate the locations of obstac…
Visual Diver Face Recognition for Underwater Human-Robot Interaction
Jungseok Hong, Sadman Sakib Enan, Christopher Morse +1
This paper presents a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwate…
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
Jungseok Hong, Michael Fulton, Junaed Sattar
This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labe…
Design and Experiments with LoCO AUV: A Low Cost Open-Source Autonomous Underwater Vehicle
Chelsey Edge, Sadman Sakib Enan, Michael Fulton +8
In this paper we present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 m…
A Generative Approach Towards Improved Robotic Detection of Marine Litter
Jungseok Hong, Michael Fulton, Junaed Sattar
This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage va…
An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots
Jungseok Hong, Michael Fulton, Junaed Sattar
This paper presents an evaluation of a number of probabilistic algorithms for localization of autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms, based on…
Robotic Detection of Marine Litter Using Deep Visual Detection Models
Michael Fulton, Jungseok Hong, Md Jahidul Islam +1
Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs)…
Person Following by Autonomous Robots: A Categorical Overview
Md Jahidul Islam, Jungseok Hong, Junaed Sattar
A wide range of human-robot collaborative applications in diverse domains such as manufacturing, health care, the entertainment industry, and social interactions, require an autono…