no code implementations • 20 Feb 2024 • Collin Hague, Nick Kakavitsas, Jincheng Zhang, Chris Beam, Andrew Willis, Artur Wolek
This paper describes the hardware design and flight demonstration of a small quadrotor with imaging sensors for urban mapping, hazard avoidance, and target tracking research.
no code implementations • 5 Feb 2024 • Andrew Willis, Collin Hague, Artur Wolek, Kevin Brink
UAV missions often require specific geometric constraints to be satisfied between ground locations and the vehicle location.
no code implementations • 15 Jan 2024 • Chris Beam, Jincheng Zhang, Nicholas Kakavitsas, Collin Hague, Artur Wolek, Andrew Willis
The impact of this can be significant, potentially allowing expansive virtual testing of robotic systems at specific deployment locations to develop solutions that are tailored to the environment and potentially outperforming solutions meant to work in completely generic environments.
1 code implementation • 12 Jan 2023 • Collin Hague, Andrew Willis, Dipankar Maity, Artur Wolek
Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN.
no code implementations • 10 Mar 2021 • Akash Chandrashekar, John Papadakis, Andrew Willis, Jamie Gantert
This article describes a technique to augment a typical RGBD sensor by integrating depth estimates obtained via Structure-from-Motion (SfM) with sensor depth measurements.
no code implementations • 23 Feb 2021 • Pengcheng Liu, Nathan Hewitt, Waseem Shadid, Andrew Willis
We propose a system to semi-automatically extract quantitative metrics that are major indicators of fracture severity.
2 code implementations • ECCV 2020 • Taojiannan Yang, Sijie Zhu, Chen Chen, Shen Yan, Mi Zhang, Andrew Willis
We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime.
no code implementations • 25 Sep 2019 • Taojiannan Yang, Sijie Zhu, Yan Shen, Mi Zhang, Andrew Willis, Chen Chen
We propose a framework to mutually learn from different input resolutions and network widths.