In June 2020,
I joined the faculty of the
School of Engineering
at the University of Aberdeen,

United Kingdom.

Dr.-Ing. Pascal Meißner

E-Mail: pascal(dot)meissner(at)alumni(dot)kit(dot)edu

2011

P. Meißner, S. R. Schmidt-Rohr, M. Lösch, R. Jäkel and R. Dillmann. Robust localization of furniture parts by integrating depth and intensity data suitable for range sensors with varying image quality. In International Conference on Advanced Robotics, 2011.

2013

P. Meißner, R. Reckling, R. Jäkel, S. R. Schmidt-Rohr and R. Dillmann. Recognizing scenes with hierarchical implicit shape models based on spatial object relations for programming by demonstration. In International Conference on Advanced Robotics, 2013.

2012

P. Meißner, S. R. Schmidt-Rohr, M. Lösch, R. Jäkel and R. Dillmann. Localization of furniture parts by integrating range and intensity data robust against depths with low signal-to-noise ratio. In Robotics and Autonomous Systems, 2012.

2015

P. Meißner, F. Hanselmann, R. Jäkel, S. R. Schmidt-Rohr and R. Dillmann. Automated selection of spatial object relations for modeling and recognizing indoor scenes with hierarchical implicit shape models. In International Conference on Intelligent Robots and Systems, 2015. [video]

asr_ros_pkgs is an open source framework in which the core findings of my Ph.D. research at the Chair of Prof. Dr.-Ing. R. Dillmann are realized. It provides a software architecture controlling a mobile robot and is based on the Robot Operating System (ROS). Research contributions which we integrated in it are designed as standalone libraries with ROS wrappers.

asr_ros_pkgs implements 'Active Scene Recognition' - an approach for mobile robots to recognize scenes in distributed and cluttered indoor environments. asr_ism implements our ISM-tree representation.

Source code for all of our contributions is available on GitHub.

An overview on our documentation can be found in the ROS Wiki.

2016

P. Meißner, R. Schleicher, R. Hutmacher, S. R. Schmidt-Rohr and R. Dillmann. Scene Recognition for Mobile Robots by Relational Object Search using Next-Best-View Estimates from Hierarchical Implicit Shape Models. In International Conference on Intelligent Robots and Systems, 2016. [video]

Learning the Context in Programming by Demonstration of Manipulation Tasks. DFG - German Research Association, 2014 - 2018, Germany.

ECHORD++ experiment - DexBuddy (Dexterous Robotic Co-Worker). European Commission (EC), 2015 - 2016, European Union.

2014

P. Meißner, R. Reckling, V. Wittenbeck, S. R. Schmidt-Rohr and R. Dillmann. Active scene recognition for programming by demonstration using next-best-view estimates from hierarchical implicit shape models. In International Conference on Robotics and Automation, 2014. [video]

2020

P. Meißner. Indoor Scene Recognition by 3-D Object Search for Robot Programming by Demonstration. Volume 135 of Springer Tracts in Advanced Robotics, 2020.