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Connor Lee

5 accepted papers

2026

MonoTher-Depth: Enhancing Thermal Depth Estimation Via Confidence-Aware Distillation

ICRA 2026poster

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 availability of labeled thermal data constrains the generalization capabilities of thermal MDE models compared to founda…

2025

A Da Vinci Open Spina Bifida Suturing Simulator with Continuum Tools for Surgeon Skills Training

IROS 2025

Open Spina Bifida (OSB) is a congenital neural tube defect that affects approximately 1 in 1000 births worldwide. Robotic in-utero OSB repair provides a minimally invasive alternative to open-surgery, which places significant strain on both baby and mother. Recent advancements in da Vinci miniature

Cited by 0SourceScholar
2024

Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots

IROS 2024poster

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 alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing…

Cited by 3SourcecodeScholar
2023

Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles

IROS 2023poster

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 cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore…

Cited by 6SourcecodeScholar
2023

Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network

ICRA 2023poster

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 network. Our method does not require any thermal annotations or co-registered RGB-thermal pairs, enabling robots to per…

Cited by 21SourcecodeScholar