activity
20172022
most citedDeep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

66 citations · 134 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 1 cited

PatchMatch-Stereo-Panorama, a fast dense reconstruction from 360° video images

Hartmut Surmann, Marc Thurow, Dominik Slomma

This work proposes a new method for real-time dense 3d reconstruction for common 360° action cams, which can be mounted on small scouting UAVs during USAR missions. The proposed me…

cs.RO2022★ 14 cited

Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning

Christian Jestel, Hartmut Surmann, Jonas Stenzel +2

Multi-robot navigation is a challenging task in which multiple robots must be coordinated simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) to…

cs.RO2022★ 11 cited

Deployment of Aerial Robots during the Flood Disaster in Erftstadt / Blessem in July 2021

Hartmut Surmann, Dominik Slomma, Robert Grafe +1

Climate change is leading to more and more extreme weather events such as heavy rainfall and flooding. This technical report deals with the question of how rescue commanders can be…

cs.RO2021★ 13 cited

Deployment of Aerial Robots after a major fire of an industrial hall with hazardous substances, a report

Hartmut Surmann, Dominik Slomma, Stefan Grobelny +1

This technical report is about the mission and the experience gained during the reconnaissance of an industrial hall with hazardous substances after a major fire in Berlin. During…

cs.RO2020★ 66 cited

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

Hartmut Surmann, Christian Jestel, Robin Marchel +3

Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation…

cs.RO2019★ 29 cited

3D mapping for multi hybrid robot cooperation

Hartmut Surmann, Nils Berninger, Rainer Worst

This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and groun…