His scientific interests lie mostly in Artificial intelligence, Computer vision, Robot, Mobile robot and Segmentation. Sven Behnke has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His research links Simultaneous localization and mapping with Computer vision.
His Robot research is multidisciplinary, incorporating perspectives in Gesture, Simulation and Human–computer interaction. While the research belongs to areas of Mobile robot, Sven Behnke spends his time largely on the problem of Humanoid robot, intersecting his research to questions surrounding Multimodal interaction. His Segmentation research includes elements of Object detection, Task, Connected component and Multi resolution.
Sven Behnke spends much of his time researching Artificial intelligence, Robot, Computer vision, Humanoid robot and Human–computer interaction. His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence. The Pattern recognition study combines topics in areas such as Artificial neural network and Noise.
His Robot research incorporates elements of Simulation and Trajectory. His work in Computer vision is not limited to one particular discipline; it also encompasses Simultaneous localization and mapping. Sven Behnke combines subjects such as Visual perception, Teleoperation and Service robot with his study of Human–computer interaction.
Sven Behnke mostly deals with Artificial intelligence, Robot, Computer vision, Segmentation and Human–computer interaction. His research in Artificial intelligence intersects with topics in Task and Pattern recognition. His Robot research is multidisciplinary, incorporating perspectives in Trajectory and Control theory.
The study incorporates disciplines such as Lidar and GRASP in addition to Computer vision. The Segmentation study combines topics in areas such as Semantics, Point cloud, Representation and Feature. His Human–computer interaction study which covers Teleoperation that intersects with Visualization.
His main research concerns Artificial intelligence, Robot, Computer vision, Segmentation and Robotics. His Artificial intelligence study combines topics in areas such as Task and GRASP. The Robot study which covers Human–computer interaction that intersects with Teleoperation.
His study in the field of Object also crosses realms of Pipeline. Sven Behnke has researched Segmentation in several fields, including Point cloud and Semantic mapping. His biological study spans a wide range of topics, including Control, Task analysis and Automotive industry.
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Evaluation of pooling operations in convolutional architectures for object recognition
Dominik Scherer;Andreas Müller;Sven Behnke.
international conference on artificial neural networks (2010)
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Jens Behley;Martin Garbade;Andres Milioto;Jan Quenzel.
international conference on computer vision (2019)
RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features
Max Schwarz;Hannes Schulz;Sven Behnke.
international conference on robotics and automation (2015)
Real-time plane segmentation using RGB-D cameras
Dirk Holz;Stefan Holzer;Radu Bogdan Rusu;Sven Behnke.
robot soccer world cup (2012)
Hierarchical Neural Networks for Image Interpretation
Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D
Dirk Holz;Alexandru E. Ichim;Federico Tombari;Radu B. Rusu.
IEEE Robotics & Automation Magazine (2015)
Multi-resolution surfel maps for efficient dense 3D modeling and tracking
Jörg Stückler;Sven Behnke.
Journal of Visual Communication and Image Representation (2014)
Online trajectory generation for omnidirectional biped walking
international conference on robotics and automation (2006)
Towards a humanoid museum guide robot that interacts with multiple persons
M. Bennewitz;F. Faber;D. Joho;M. Schreiber.
ieee-ras international conference on humanoid robots (2005)
Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks.
Jörg Wagner;Volker Fischer;Michael Herman;Sven Behnke.
the european symposium on artificial neural networks (2016)
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