Nils Rottmann, M.Sc.

Ph.D. Student at the University of Luebeck

Short bio: With January 2018, Nils Rottmann is a PhD student and research scientist at the Institute for Robotics and Cognitive Systems at the University of Luebeck. In his doctoral study, with the title “Smart Sensor, Navigation and Learning Strategies for low-cost lawn care Systems”, he develops low-cost sensor systems and investigates probabilistic learning and modeling approaches. His research addresses the challenges of learning adaptive control strategies from few and sparse data and to predict and plan complex motions in dynamical systems.

He holds a master’s degree in Theoretical Mechanical Engineering from the Hamburg University of Technology, Germany. Nils Rottmann graduated with honors in December 2017 with a thesis entitled „Geometric Control and Stochastic Trajectory Planning for Underwater Robotic Systems“.

Research Interests

  • Robotics: Mobile Robotics, Sensor Development, Robot-Operating-System (ROS), Mobile Navigation, Path Planning, Complete Coverage Path Planning, Probabilistic Robotics.
  • Machine Learning: Non-Linear Regression, Graphical Models, Probabilistic Inference, Variational Inference, Gaussian Processes, Bayesian Optimization.

Contact & Quick Links

M.Sc. Nils Rottmann
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since March 2018.
Ratzeburger Allee 160,
23562 Lübeck,
Deutschland

Phone:  +49 451 3101 – 5222 
Email:   rottmann@rob.uni-luebeck.de
Web:  https://nrottmann.github.io

CV of M.Sc. Nils Rottmann
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Publcations

2021

Rottmann, N.; Denz, R.; Bruder, R.; Rueckert, E.

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Inproceedings

In: European Conference on Mobile Robots (ECMR 2021), 2021.

Links | BibTeX

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems

Cansev, Mehmet Ege; Xue, Honghu; Rottmann, Nils; Bliek, Adna; Miller, Luke E.; Rueckert, Elmar; Beckerle, Philipp

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article

In: Advanced Intelligent Systems, pp. 1–28, 2021.

Links | BibTeX

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience

2020

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Inproceedings

In: IEEE SENSORS , pp. 1–4, 2020.

Links | BibTeX

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors

Rottmann, N.; Kunavar, T.; Babič, J.; Peters, J.; Rueckert, E.

Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings

In: International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020.

Links | BibTeX

Learning Hierarchical Acquisition Functions for Bayesian Optimization

Rottmann, N.; Bruder, R.; Xue, H.; Schweikard, A.; Rueckert, E.

Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings

In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020.

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Parameter Optimization for Loop Closure Detection in Closed Environments

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification Journal Article

In: IEEE Sensors Journal, 2020.

Links | BibTeX

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E.

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

2019

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Loop Closure Detection in Closed Environments Inproceedings

In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9.

Links | BibTeX

Loop Closure Detection in Closed Environments

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Inproceedings

In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019).

Links | BibTeX

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors