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arXiv 2024年

作者： Jiao, Jianhao He, Jinhao Liu, Changkun Aegidius, Sebastian Hu, Xiangcheng Braud, Tristan Kanoulas, Dimitrios The Robot Perception and Learning Lab Intelligent Robotics Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Nansha District Guangzhou China The Department of Computer Science and Engineering HKUST Hong Kong The Department of Electronic an Computer Engineering HKUST Hong Kong The AI Centre Department of Computer Science University College London Gower Street LondonWC1E 6BT United Kingdom Archimedes/Athena RC Greece

This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available at https://***/LiteVLoc. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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Towards precise pruning points detection using semantic-instance-aware plant models for grapevine winter pruning automation

arXiv

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arXiv 2021年

作者： Fernandes, Miguel Scaldaferri, Antonello Guadagna, Paolo Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Informatics Bioengineering Robotics and System Engineering Università di Genoa Viale Causa 13 Genova16145 Italy Department of Sustainable Crop Production Università Cattolica del Sacro Cuore Via Emilia Parmense 84 Piacenza29122 Italy Italy lab Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Chung Chi Rd Ma Liu Shui Hong Kong

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity makes it time consuming. It is an operation that requires about 80-120 hours per hectare annually, making an automated robotic system that helps in speeding up the process a crucial tool in large-size vineyards. We will describe (a) a novel expert annotated dataset for grapevine segmentation, (b) a state of the art neural network implementation and (c) generation of pruning points following agronomic rules, leveraging the simplified structure of the plant. With this approach, we are able to generate a set of pruning points on the canes, paving the way towards a correct automation of grapevine winter pruning. © 2021, CC BY.

关键词： Automation

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Grapevine Winter pruning automation: On potential pruning points detection through 2D plant modeling using grapevine segmentation

arXiv

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arXiv 2021年

作者： Fernandes, Miguel Scaldaferri, Antonello Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Informatics Bioengineering Robotics and System Engineering Università di Genoa Viale Causa 13 Genova16145 Italy Italy Department of Sustainable Crop Production Università Cattolica del Sacro Cuore Via Emilia Parmense 84 Piacenza29122 Italy lab Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Mechanical and Automation Engineering T-Stone Robotics Institute Chinese University of Hong Kong Chung Chi Rd Ma Liu Shui Hong Kong

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points. © 2021, CC BY.

关键词： Automation

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