4 citations · 4 across the 6 of their papers we have counts for
8 papers
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft
Elena Sorina Lupu, Hannah Grauer, Phaedrus Leeds +9
Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations. A key enabler of such aut…
MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation
Xingxing Zuo, Nikhil Ranganathan, Connor Lee +2
Monocular depth estimation (MDE) from thermal images is a crucial technology for robotic systems operating in challenging conditions such as fog, smoke, and low light. The limited…
Vision-Based Detection of Uncooperative Targets and Components on Small Satellites
Hannah Grauer, Elena-Sorina Lupu, Connor Lee +5
Space debris and inactive satellites pose a threat to the safety and integrity of operational spacecraft and motivate the need for space situational awareness techniques. These unc…
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
Connor Lee, Saraswati Soedarmadji, Matthew Anderson +2
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products…
Caltech Aerial RGB-Thermal Dataset in the Wild
Connor Lee, Matthew Anderson, Nikhil Raganathan +4
We present the first publicly-available RGB-thermal dataset designed for aerial robotics operating in natural environments. Our dataset captures a variety of terrain across the Uni…
RGB-X Object Detection via Scene-Specific Fusion Modules
Sri Aditya Deevi, Connor Lee, Lu Gan +3
Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing dee…
Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles
Connor Lee, Jonathan Gustafsson Frennert, Lu Gan +2
We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cu…
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
Lu Gan, Connor Lee, Soon-Jo Chung
This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention…