2023 - Research.com Computer Science in Germany Leader Award
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.
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.
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.
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.
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
G. Grisetti;C. Stachniss;W. Burgard.
IEEE Transactions on Robotics (2007)
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
Armin Hornung;Kai M. Wurm;Maren Bennewitz;Cyrill Stachniss.
Autonomous Robots (2013)
Coordinated multi-robot exploration
W. Burgard;M. Moors;C. Stachniss;F.E. Schneider.
IEEE Transactions on Robotics (2005)
A Tutorial on Graph-Based SLAM
G Grisetti;R Kümmerle;C Stachniss;W Burgard.
IEEE Intelligent Transportation Systems Magazine (2010)
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)
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
Cyrill Stachniss;Giorgio Grisetti;Wolfram Burgard.
robotics science and systems (2005)
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Jens Behley;Martin Garbade;Andres Milioto;Jan Quenzel.
international conference on computer vision (2019)
On measuring the accuracy of SLAM algorithms
Rainer Kümmerle;Bastian Steder;Christian Dornhege;Michael Ruhnke.
Autonomous Robots (2009)
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)
RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation
Andres Milioto;Ignacio Vizzo;Jens Behley;Cyrill Stachniss.
intelligent robots and systems (2019)
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