Thomas Wiedemann — accepted AI-conference papers

2025

Deep Learning Based Topography Aware Gas Source Localization with Mobile Robot

Changhao Tian, Annan Wang, Han Fan, Thomas Wiedemann, Yifei Luo, Le Yang +3

ICRA 2025

Gas source localization in complex environments is critical for applications such as environmental monitoring, industrial safety, and disaster response. Traditional methods often struggle with the challenges posed by a lack of environmental topography integration, especially when interactions betwee

Cited by 0 Source ↗Scholar ↗

2016

Decentralized multi-agent exploration with online-learning of Gaussian processes

Alberto Viseras Ruiz, Thomas Wiedemann, Christoph Manss, Lukas Magel, Joachim Müller, Dmitriy Shutin +1

ICRA 2016

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi-ag

Cited by 68 Source ↗Scholar ↗