Search Results - "robotics and automation in agriculture and forestry"

Spatio-Temporal Consistent Semantic Mapping for Robotics Fruit Growth Monitoring

Authors: Luca Lobefaro Matteo Sodano Daniel Fusaro et al.

Source: IEEE Robotics and Automation Letters. 10:9470-9477

Subject Terms: Mapping, robotics and automation in agriculture and forestry

Aerial Image-based Inter-day Registration for Precision Agriculture

Authors: Gao, Chen Daxinger, Franz Roth, Lukas et al.

Source: 2024 IEEE International Conference on Robotics and Automation (ICRA). :11862-11868

Subject Terms: Agricultural automation, Robotics and automation in agriculture and forestry, Aerial systems: Perception and autonomy

Improving Robotic Fruit Harvesting Within Cluttered Environments Through 3D Shape Completion

Authors: Federico Magistri Yue Pan Jake Bartels et al.

Source: IEEE Robotics and Automation Letters. 9:7357-7364

Subject Terms: Pipelines, Grasping, Point cloud compression, Robotics and Automation in Agriculture and Forestry, Agricultural Automation, Shape, Three-dimensional displays, Perception for Grasping and Manipulation, Robots, End effectors

Focusing on Object Extremities for Tree Instance Segmentation in Forest Environments

Authors: Robin Condat Pascal Vasseur Guillaume Allibert et al.

Contributors: Robin Condat Pascal Vasseur Guillaume Allibert et al.

Source: IEEE Robotics and Automation Letters. 9:5480-5487

Subject Terms: Vegetation, Robotics and Automation in Agriculture and Forestry, Task analysis, [SDE]Environmental Sciences, Instance segmentation, Three-dimensional displays, Forestry, Extremities, 15. Life on land, Real-time systems, Deep Learning for Visual Perception, [SPI.AUTO]Engineering Sciences [physics]/Automatic

Learning Occluded Branch Depth Maps in Forest Environments Using RGB-D Images

Authors: Christian Geckeler Emanuele Aucone Yannick Schnider et al.

Source: IEEE Robotics and Automation Letters, 9 (3)

Subject Terms: Deep learning for visual perception, Robotics and automation in agriculture and forestry, RGB-D perception

File Description: application/application/pdf

Access URL: http://hdl.handle.net/20.500.11850/659145

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Authors: Kan, Xinyue Thayer, Thomas C Carpin, Stefano et al.

Source: IEEE Robotics and Automation Letters. 6(2)

Subject Terms: 4605 Data Management and Data Science (for-2020), 46 Information and Computing Sciences (for-2020), Planning, robotics and automation in agriculture and forestry, scheduling and coordination, task and motion planning, cs.RO, cs.RO, 0913 Mechanical Engineering (for), 4007 Control engineering, mechatronics and robotics (for-2020), 4602 Artificial intelligence (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/2557z5w3
https://escholarship.org/content/qt2557z5w3/qt2557z5w3.pdf

APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS

Authors: Stefan de Jong Hilmy Baja Karsjen Tamminga et al.

Source: IEEE Robotics and Automation Letters. 7:11418-11425

Subject Terms: Object detection, Robotics and Automation in Agriculture and Forestry, Agricultural Automation, Annotations, Task analysis, 0202 electrical engineering, electronic engineering, information engineering, Feature extraction, Three-dimensional displays, Training, Wearable sensors, Object Detection, Segmentation and Categorization, 02 engineering and technology, Deep Learning Methods

Reinforcement Learning Control of a Forestry Crane Manipulator

Authors: Andersson, Jennifer Bodin, Kenneth Lindmark, Daniel et al.

Source: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). :2121-2126

Subject Terms: Reinforcement Learning, Robotics and Automation in Agriculture and Forestry, Deep Learning in Grasping and Manipulation, fysik, Physics, Computer Science, datalogi

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-187948
https://umu.diva-portal.org/smash/get/diva2:1597673/FULLTEXT01.pdf

Learning Occluded Branch Depth Maps in Forest Environments Using RGB-D Images

Authors: Geckeler, Christian Aucone, Emanuele Schnider, Yannick et al.

Source: IEEE Robotics and Automation Letters, 9 (3)

Subject Terms: Deep learning for visual perception, Robotics and automation in agriculture and forestry, RGB-D perception

File Description: application/application/pdf

Relation: info:eu-repo/semantics/altIdentifier/wos/001167554600002; info:eu-repo/grantAgreement/SNF/Eccellenza/186865; http://hdl.handle.net/20.500.11850/659145

Availability: https://hdl.handle.net/20.500.11850/659145
https://doi.org/10.3929/ethz-b-000659145

INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields

Authors: Hindel, Julia Gosala, Nikhil Bregler, Kevin et al.

Subject Terms: Computer vision for automation, deep learning for visual perception, robotics and automation in agriculture and forestry

Relation: IEEE robotics and automation letters; #PLACEHOLDER_PARENT_METADATA_VALUE#; https://publica.fraunhofer.de/handle/publica/466912

Availability: https://publica.fraunhofer.de/handle/publica/466912
https://doi.org/10.1109/LRA.2023.3301269

APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS

Authors: de Jong, Stefan Baja, Hilmy Tamminga, Karsjen et al.

Source: ISSN: 2377-3766

Index Terms: Agricultural Automation, Annotations, Deep Learning Methods, Feature extraction, Object Detection, Segmentation and Categorization, Object detection, Robotics and Automation in Agriculture and Forestry, Task analysis, Three-dimensional displays, Training, Wearable sensors, Article/Letter to editor

URL: https://edepot.wur.nl/577473
https://edepot.wur.nl/577473