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Performance of RGB-D camera for different object types in greenhouse conditions
Umeå University, Umeå, Sweden.ORCID iD: 0000-0002-4600-8652
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems)ORCID iD: 0000-0003-4685-379x
Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0002-7430-8468
2019 (English)In: 2019 European Conference on Mobile Robots (ECMR) / [ed] Libor Přeučil, Sven Behnke, Miroslav Kulich, IEEE, 2019, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

RGB-D cameras play an increasingly important role in localization and autonomous navigation of mobile robots. Reasonably priced commercial RGB-D cameras have recently been developed for operation in greenhouse and outdoor conditions. They can be employed for different agricultural and horticultural operations such as harvesting, weeding, pruning and phenotyping. However, the depth information extracted from the cameras varies significantly between objects and sensing conditions. This paper presents an evaluation protocol applied to a commercially available Fotonic F80 time-of-flight RGB-D camera for eight different object types. A case study of autonomous sweet pepper harvesting was used as an exemplary agricultural task. Each of the objects chosen is a possible item that an autonomous agricultural robot must detect and localize to perform well. A total of 340 rectangular regions of interests (ROI) were marked for the extraction of performance measures of point cloud density, and variability around center of mass, 30-100 ROIs per object type. An additional 570 ROIs were generated (57 manually and 513 replicated) to evaluate the repeatability and accuracy of the point cloud. A statistical analysis was performed to evaluate the significance of differences between object types. The results show that different objects have significantly different point density. Specifically metallic materials and black colored objects had significantly less point density compared to organic and other artificial materials introduced to the scene as expected. The point cloud variability measures showed no significant differences between object types, except for the metallic knife that presented significant outliers in collected measures. The accuracy and repeatability analysis showed that 1-3 cm errors are due to the the difficulty for a human to annotate the exact same area and up to ±4 cm error is due to the sensor not generating the exact same point cloud when sensing a fixed object.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1-6
Keywords [en]
agriculture, cameras, feature extraction, greenhouses, image colour analysis, image sensors, industrial robots, mobile robots, object tracking, robot vision, statistical analysis, pruning, sensing conditions, evaluation protocol, object types, autonomous sweet pepper harvesting, exemplary agricultural task, autonomous agricultural robot, ROI, point cloud density, object type, point density, black colored objects, point cloud variability measures, fixed object, greenhouse conditions, autonomous navigation, mobile robots, agricultural operations, horticultural operations, commercial RGB-D cameras, Fotonic F80 time-of-flight RGB-D camera, size 4.0 cm, size 1.0 cm to 3.0 cm, Cameras, Three-dimensional displays, Robot vision systems, End effectors, Green products
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-79409DOI: 10.1109/ECMR.2019.8870935Scopus ID: 2-s2.0-85074395978ISBN: 978-1-7281-3605-9 (electronic)ISBN: 978-1-7281-3606-6 (print)OAI: oai:DiVA.org:oru-79409DiVA, id: diva2:1388687
Conference
2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic, 4-6 Sept. 2019
Funder
Knowledge Foundation
Note

This research received funding from the Swedish Knowledge Foundation (KKS) under the Semantic Robots research profile and was partially supported by the European Commission (SWEEPER GA no. 66313) and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering.

Available from: 2020-01-27 Created: 2020-01-27 Last updated: 2020-01-29Bibliographically approved

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Kurtser, Polina

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