H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 60 Citations 18,294 213 World Ranking 1536 National Ranking 54
Electronics and Electrical Engineering H-index 49 Citations 14,715 138 World Ranking 1217 National Ranking 40

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary scientific interests are in Artificial intelligence, Robot, Mobile robot, Computer vision and Simultaneous localization and mapping. His study brings together the fields of Algorithm and Artificial intelligence. His Robot research incorporates themes from Machine learning, Representation, Probabilistic logic and Human–computer interaction.

In his study, Real-time computing, Theoretical computer science, Stereopsis and Monocular vision is strongly linked to Motion planning, which falls under the umbrella field of Mobile robot. His Computer vision research is multidisciplinary, incorporating perspectives in Grid and Robot kinematics. His research investigates the connection between Simultaneous localization and mapping and topics such as Gradient descent that intersect with problems in Minimization problem and Stochastic gradient descent.

His most cited work include:

  • Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters (1419 citations)
  • OctoMap: an efficient probabilistic 3D mapping framework based on octrees (1364 citations)
  • Coordinated multi-robot exploration (862 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Robot, Computer vision, Mobile robot and Robotics are his primary areas of study. He combines topics linked to Machine learning with his work on Artificial intelligence. In general Robot study, his work on Motion planning often relates to the realm of Field, thereby connecting several areas of interest.

In his research on the topic of Computer vision, Benchmark is strongly related with Lidar. His Mobile robot research includes elements of Grid and Probabilistic logic. His Simultaneous localization and mapping study frequently draws connections between adjacent fields such as Algorithm.

He most often published in these fields:

  • Artificial intelligence (68.06%)
  • Robot (53.47%)
  • Computer vision (43.75%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (68.06%)
  • Robot (53.47%)
  • Computer vision (43.75%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Robot, Computer vision, Segmentation and Precision agriculture. His study connects Machine learning and Artificial intelligence. His work on Mobile robot as part of general Robot research is frequently linked to Field, thereby connecting diverse disciplines of science.

His Mobile robot research includes themes of Graphical model and Probabilistic logic. His work carried out in the field of Computer vision brings together such families of science as Lidar and Odometry. He has researched Segmentation in several fields, including Object and Deep learning.

Between 2017 and 2021, his most popular works were:

  • SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (200 citations)
  • RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation (137 citations)
  • Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs (108 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Cyrill Stachniss mainly investigates Artificial intelligence, Computer vision, Segmentation, Point cloud and Precision agriculture. Robot, Convolutional neural network, RGB color model, Visualization and Leverage are subfields of Artificial intelligence in which his conducts study. His work on Matching and Ground truth as part of general Computer vision study is frequently linked to Process and Hash function, bridging the gap between disciplines.

His Point cloud research focuses on Lidar and how it relates to Benchmark, Task, Odometry and Loop closing. His studies in Odometry integrate themes in fields like Simultaneous localization and mapping and Particle filter. While the research belongs to areas of Precision agriculture, he spends his time largely on the problem of Agrochemical, intersecting his research to questions surrounding Sustainable agriculture.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

G. Grisetti;C. Stachniss;W. Burgard.
IEEE Transactions on Robotics (2007)

1993 Citations

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

Armin Hornung;Kai M. Wurm;Maren Bennewitz;Cyrill Stachniss.
Autonomous Robots (2013)

1616 Citations

Coordinated multi-robot exploration

W. Burgard;M. Moors;C. Stachniss;F.E. Schneider.
IEEE Transactions on Robotics (2005)

1210 Citations

A Tutorial on Graph-Based SLAM

G Grisetti;R Kümmerle;C Stachniss;W Burgard.
IEEE Intelligent Transportation Systems Magazine (2010)

902 Citations

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

G. Grisettiyz;C. Stachniss;W. Burgard.
international conference on robotics and automation (2005)

868 Citations

Information Gain-based Exploration Using Rao-Blackwellized Particle Filters

Cyrill Stachniss;Giorgio Grisetti;Wolfram Burgard.
robotics science and systems (2005)

507 Citations

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

Giorgio Grisetti;Cyrill Stachniss;Slawomir Grzonka;Wolfram Burgard.
robotics science and systems (2007)

336 Citations

On measuring the accuracy of SLAM algorithms

Rainer Kümmerle;Bastian Steder;Christian Dornhege;Michael Ruhnke.
Autonomous Robots (2009)

322 Citations

Supervised Learning of Places from Range Data using AdaBoost

O.M. Mozos;C. Stachniss;W. Burgard.
international conference on robotics and automation (2005)

290 Citations

Nonlinear Constraint Network Optimization for Efficient Map Learning

G. Grisetti;C. Stachniss;W. Burgard.
IEEE Transactions on Intelligent Transportation Systems (2009)

277 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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