Forestry Crane Automation using Learning-based Visual Grasping Point Prediction-Reference-Cited by-同舟云学术

Forestry Crane Automation using Learning-based Visual Grasping Point Prediction

Published:2022-08-01 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Sensors Applications Symposium (SAS)
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Author:

Gietler Harald¹,Bohm Christoph¹,Ainetter Stefan²,Schoffmann Christian¹,Fraundorfer Friedrich²,Weiss Stephan¹,Zangl Hubert¹

Affiliation:

1. University of Klagenfurt,Institute of Smart Systems Technologies,Austria

2. Graz University of Technology,Institute of Computer Graphics and Vision,Austria

Funder

European Regional Development Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9881325/9881241/09881370.pdf?arnumber=9881370

Reference19 articles.

1. Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks;ainetter;British Machine Vision Conf (BMVC),2021

2. Scalable Retrofit Angular Position Sensor System

3. Grasping Point Prediction in Cluttered Environment using Automatically Labeled Data;ainetter;2020 Joint Austrian Computer Vision and Robotics Workshop,2020

4. Advances in Neural Information Processing Systems (NIPS);ren,2015

5. Deep Residual Learning for Image Recognition;he;Proceedings of the IEEE Conf on Computer Vision and Pattern Recognition,2016

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1. Log Loading Automation for Timber-Harvesting Industry;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Real-time target point identification and automated log grasping by a forwarder, using a single stereo camera for both object detection and boom-tip control;Silva Fennica;2024

3. Realization of Autonomous Detection, Positioning and Angle Estimation of Harvested Logs;Croatian journal of forest engineering;2023-06-07

4. Design and Evaluation of Capacitive Smart Transducer for a Forestry Crane Gripper;Sensors;2023-03-02