no code implementations • 30 Mar 2021 • Timo Hinzmann, Roland Siegwart
This paper introduces SD-6DoF-ICLK, a learning-based Inverse Compositional Lucas-Kanade (ICLK) pipeline that uses sparse depth information to optimize the relative pose that best aligns two images on SE(3).
1 code implementation • 2020 Conference on Robot Learning 2020 • Florian Achermann, Andrey Kolobov, Debadeepta Dey, Timo Hinzmann, Jen Jen Chung, Roland Siegwart, Nicholas Lawrance
This model is then deployed for fast and accurate online interest point detection.
no code implementations • 11 Aug 2020 • Timo Hinzmann, Roland Siegwart
This paper presents a framework for the localization of Unmanned Aerial Vehicles (UAVs) in unstructured environments with the help of deep learning.
no code implementations • 10 Aug 2020 • Timo Hinzmann, Tobias Stegemann, Cesar Cadena, Roland Siegwart
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude.
no code implementations • 11 Mar 2018 • Marius Huber, Timo Hinzmann, Roland Siegwart, Larry H. Matthies
In this work, we propose that the range error is cubic in range for stereo systems with integrated illuminators.
no code implementations • 19 Dec 2017 • Timo Hinzmann, Tim Taubner, Roland Siegwart
This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV).
no code implementations • 11 Oct 2016 • Mathias Gehrig, Elena Stumm, Timo Hinzmann, Roland Siegwart
We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation.