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RGB-D datasets for robotic perception in site-specific agricultural operations: A survey
Örebro University, School of Science and Technology. Department of Radiation Science, Radiation Physics, Umeå University, Sweden. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-4685-379X
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-3788-499X
2023 (English)In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 212, article id 108035Article, review/survey (Refereed) Published
Abstract [en]

Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 212, article id 108035
Keywords [en]
3D perception, Color point clouds, Datasets, Computer vision, Agricultural robotics
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-108413DOI: 10.1016/j.compag.2023.108035ISI: 001059437100001Scopus ID: 2-s2.0-85172469543OAI: oai:DiVA.org:oru-108413DiVA, id: diva2:1800391
Available from: 2023-09-26 Created: 2023-09-26 Last updated: 2023-12-08Bibliographically approved

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Kurtser, PolinaLowry, Stephanie

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