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Cyrill Stachniss

Cyrill Stachniss

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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
84
Citations
36868
World Ranking
830
National Ranking
29

Electronics and Electrical Engineering

D-Index
83
Citations
36492
World Ranking
419
National Ranking
12

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Cyrill Stachniss is affiliated with the University of Bonn in Germany. Their research spans multiple fields with a focus on engineering and computer science. They have contributed extensively to subfields such as computer vision and pattern recognition, aerospace engineering, plant science, environmental engineering, and geology.

The primary topics of their work include robotics and sensor-based localization, smart agriculture and AI, remote sensing and LiDAR applications, robotic path planning algorithms, 3D surveying and cultural heritage, advanced image and video retrieval techniques, and advanced neural network applications.

Their recent notable publications include:

  • "KISS-ICP: In Defense of Point-to-Point ICP - Simple, Accurate, and Robust Registration If Done the Right Way," 2023, IEEE Robotics and Automation Letters
  • "Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data," 2021, IEEE Robotics and Automation Letters
  • "Robotic weed control using automated weed and crop classification," 2020, Journal of Field Robotics
  • "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis," 2021, PLoS ONE
  • "Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset," 2021, The International Journal of Robotics Research

The frequent co-authors who have collaborated on a significant number of publications with Cyrill Stachniss include:

  • Jens Behley
  • Federico Magistri
  • Xieyuanli Chen
  • Marija Popović
  • Tiziano Guadagnino

Publication venues where Cyrill Stachniss has contributed the most encompass arXiv (Cornell University), IEEE Robotics and Automation Letters, and the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) for the years 2021 and 2022. Other venues include Computers and Electronics in Agriculture.

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Computers and Electronics in Agriculture

Best Publications

  • OctoMap: an efficient probabilistic 3D mapping framework based on octrees

    Armin Hornung;Kai M. Wurm;Maren Bennewitz;Cyrill Stachniss

  • Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

    G. Grisetti;C. Stachniss;W. Burgard

  • SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

    Jens Behley;Martin Garbade;Andres Milioto;Jan Quenzel

  • A Tutorial on Graph-Based SLAM

    G Grisetti;R Kümmerle;C Stachniss;W Burgard

  • Coordinated multi-robot exploration

    W. Burgard;M. Moors;C. Stachniss;F.E. Schneider

  • RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

    Andres Milioto;Ignacio Vizzo;Jens Behley;Cyrill Stachniss

  • Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling

    G. Grisettiyz;C. Stachniss;W. Burgard

  • Information Gain-based Exploration Using Rao-Blackwellized Particle Filters

    Cyrill Stachniss;Giorgio Grisetti;Wolfram Burgard

  • SuMa++: Efficient LiDAR-based Semantic SLAM

    Xieyuanli Chen;Andres Milioto;Emanuele Palazzolo;Philippe Giguere

  • Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

    Jens Behley;Cyrill Stachniss

  • On measuring the accuracy of SLAM algorithms

    Rainer Kümmerle;Bastian Steder;Christian Dornhege;Michael Ruhnke

  • KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way

    Unknown

  • UAV-based crop and weed classification for smart farming

    Philipp Lottes;Raghav Khanna;Johannes Pfeifer;Roland Siegwart

  • Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

    Andres Milioto;Philipp Lottes;Cyrill Stachniss

  • A tree parameterization for efficiently computing maximum likelihood maps using gradient descent

    Giorgio Grisetti;Cyrill Stachniss;Slawomir Grzonka;Wolfram Burgard

  • Robust map optimization using dynamic covariance scaling

    Pratik Agarwal;Gian Diego Tipaldi;Luciano Spinello;Cyrill Stachniss

  • Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields

    Nived Chebrolu;Philipp Lottes;Alexander Schaefer;Wera Winterhalter

  • Coordinated multi-robot exploration using a segmentation of the environment

    K.M. Wurm;C. Stachniss;W. Burgard

  • Supervised Learning of Places from Range Data using AdaBoost

    O.M. Mozos;C. Stachniss;W. Burgard

  • Nonlinear Constraint Network Optimization for Efficient Map Learning

    G. Grisetti;C. Stachniss;W. Burgard

  • Hierarchical optimization on manifolds for online 2D and 3D mapping

    Giorgio Grisetti;Rainer Kummerle;Cyrill Stachniss;Udo Frese

  • An Efficient Probabilistic 3D Mapping Framework Based on Octrees

    Armin Hornung;Kai M. Wurm;Maren Bennewitz;Cyrill Stachniss

Frequent Co-Authors

Wolfram Burgard
Wolfram Burgard University of Technology Nuremberg
Giorgio Grisetti
Giorgio Grisetti Sapienza University of Rome
Oliver Brock
Oliver Brock Technical University of Berlin
Maren Bennewitz
Maren Bennewitz University of Bonn
Sven Behnke
Sven Behnke University of Bonn
Juan Nieto
Juan Nieto Microsoft (United States)
Jürgen Sturm
Jürgen Sturm Google (United States)
Achim Walter
Achim Walter ETH Zurich
Matthias Teschner
Matthias Teschner University of Freiburg

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