9 citations · 15 across the 24 of their papers we have counts for
16 papers · 1 filter
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…
TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images
Spencer Carmichael, Katherine A. Skinner
Thermal cameras offer several advantages for simultaneous localization and mapping (SLAM) with mobile robots: they provide a passive, low-power solution to operating in darkness, a…
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.…
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
Anja Sheppard, Parker Ewen, Joey Wilson +6
This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures fr…
A Stochastic Approach to Terrain Maps for Safe Lunar Landing
Anja Sheppard, Chris Reale, Katherine A. Skinner
Safely landing on the lunar surface is a challenging task, especially in the heavily-shadowed South Pole region where traditional vision-based hazard detection methods are not reli…
Field Report on Ground Penetrating Radar for Localization at the Mars Desert Research Station
Anja Sheppard, Katherine A. Skinner
In this field report, we detail the lessons learned from our field expedition to collect Ground Penetrating Radar (GPR) data in a Mars analog environment for the purpose of validat…
MarsLGPR: Mars Rover Localization with Ground Penetrating Radar
Anja Sheppard, Katherine A. Skinner
In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetar…
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…
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…
Learning Surface Terrain Classifications from Ground Penetrating Radar
Anja Sheppard, Jason Brown, Nilton Renno +1
Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While…
A Detection and Filtering Framework for Collaborative Localization
Thirumalaesh Ashokkumar, Katherine A Skinner, Siddarth Agarwal +2
Increasingly, autonomous vehicles (AVs) are becoming a reality, such as the Advanced Driver Assistance Systems (ADAS) in vehicles that assist drivers in driving and parking functio…
LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
Jingyu Song, Lingjun Zhao, Katherine A. Skinner
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabili…
Learning Which Side to Scan: Multi-View Informed Active Perception with Side Scan Sonar for Autonomous Underwater Vehicles
Advaith V. Sethuraman, Philip Baldoni, Katherine A. Skinner +1
Autonomous underwater vehicles often perform surveys that capture multiple views of targets in order to provide more information for human operators or automatic target recognition…
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…
Automatic Data Processing for Space Robotics Machine Learning
Anja Sheppard, Katherine A. Skinner
Autonomous terrain classification is an important problem in planetary navigation, whether the goal is to identify scientific sites of interest or to traverse treacherous areas saf…