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zaenker

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dengler

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Menon

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Sicong

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Roychoudhury

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Jacob

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Chenchani

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Phenorob - Robotics and Phenotyping for Sustainable Crop Production

PhenoRob performs world-leading research in robotics and phenotyping for sustainable crop production. Our vision is to transform crop production by optimizing breeding and farming management through developing and deploying new technologies. PhenoRob addresses a real-world problem with a technology-oriented approach. Our scientists have backgrounds in computer science, geodesy, robotics, plant science, soil science, economics, and environmental science. This interdisciplinary team forms the only DFG-funded Cluster of Excellence focusing on agriculture.
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Active Perception

Active perception tries to answer the question of where to look at to gain more information. This is an important task in any application where occlusions appear and scenes need to be observed from different angles. Especially for robots operating in complex unstructured environments, active perception is key to efficient spatio-temporal mapping.
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AID4Crops - Automation and AI for Monitoring and Decision Making of Horticultural Crops

AID4Crops will bring together research about what can be sensed with what should be sensed. To do this, we will develop novel AI algorithms to enable autonomous monitoring (sensing) and management (forecasting and decision making) for horticultural crops. These approaches will be deployed in horticulture as it provides a set of realistic yet challenging environments with crops grown both indoors (in glasshouses) and outdoor (in orchards); the indoor environment provides greater control over the growth of the crops.
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Map Space Belief Prediction for Manipulation-Enhanced Mapping

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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

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EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images

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Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits

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DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction

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Graph-based View Motion Planning for Fruit Detection

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NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction

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Viewpoint Push Planning for Mapping of Unknown Confined Spaces

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Perception for Humanoid Robots

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Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications

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Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

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Viewpoint Planning for Fruit Size and Position Estimation

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PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention.

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Online Object-Oriented Semantic Mapping and Map Updating

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Combining Local and Global Viewpoint Planning for Fruit Coverage

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Contact:

Prof. Dr. Maren Bennewitz
Humanoid Robots Lab
University of Bonn
Friedrich-Hirzebruch-Allee 8
53115 Bonn

Tel.: +49 (0) 228 73-54164
Secr.: +49 (0) 228 73-4422
Fax.: +49 (0) 228 73-4425
Email: maren_at_cs.uni-bonn.de

Office: 2.022

University of Bonn
Institute for Computer Science
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