9 citations · 10 across the 7 of their papers we have counts for
9 papers
Towards Scaling Marine Perception with Synthetic Data
Haoyu Ma, Onur Bagoren, Anja Sheppard +6
Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is often expensive and laborious to…
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
Onur Bagoren, Seth Isaacson, Sacchin Sundar +6
Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks.…
SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting
Advaith V. Sethuraman, Max Rucker, Onur Bagoren +3
In this paper, we present SonarSplat, a novel Gaussian splatting framework for imaging sonar that demonstrates realistic novel view synthesis and models acoustic streaking phenomen…
OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
Jingyu Song, Haoyu Ma, Onur Bagoren +3
Underwater simulators offer support for building robust underwater perception solutions. Significant work has recently been done to develop new simulators and to advance the perfor…
PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments
Onur Bagoren, Marc Micatka, Katherine A. Skinner +1
When navigating and interacting in challenging environments where sensory information is imperfect and incomplete, robots must make decisions that account for these shortcomings. W…
VAIR: Visuo-Acoustic Implicit Representations for Low-Cost, Multi-Modal Transparent Surface Reconstruction in Indoor Scenes
Advaith V. Sethuraman, Onur Bagoren, Harikrishnan Seetharaman +3
Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and…
TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle
Jingyu Song, Onur Bagoren, Razan Andigani +2
Significant work has been done on advancing localization and mapping in underwater environments. Still, state-of-the-art methods are challenged by low-texture environments, which i…
Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark
Advaith V. Sethuraman, Anja Sheppard, Onur Bagoren +4
Open-source benchmark datasets have been a critical component for advancing machine learning for robot perception in terrestrial applications. Benchmark datasets enable the widespr…
Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots
Jingyu Song, Onur Bagoren, Katherine A. Skinner
For underwater vehicles, robotic applications have the added difficulty of operating in highly unstructured and dynamic environments. Environmental effects impact not only the dyna…