Extraction Spreadsheet for a Systematic Literature Review on Real-Time Object Detection in Ground Mobile Robots (2022–2025)

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1. Verfasser: Câmara, Ricardo
Format: Recurso digital
Veröffentlicht: Zenodo 2026
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author Câmara, Ricardo
author_facet Câmara, Ricardo
contents <p>This Zenodo deposit provides the extraction spreadsheet and derived coded dataset used in a systematic literature review (SLR) on real-time object detection for ground (terrestrial) mobile robots, covering primary studies published from 2022 to 2025. The dataset compiles bibliographic metadata and structured variables extracted from the selected papers, including task/application context, sensing modality, deployment locus (e.g., onboard/edge), hardware platform, detector family/model information, and evaluation/reporting attributes.</p> <p>The spreadsheet also preserves “not reported” cases to reflect the original reporting in the primary studies and to support transparency in aggregation. A column-by-column data dictionary is included to document field definitions and coding conventions, enabling reuse and independent verification of the quantitative summaries reported in the paper.</p>
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publishDate 2026
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spellingShingle Extraction Spreadsheet for a Systematic Literature Review on Real-Time Object Detection in Ground Mobile Robots (2022–2025)
Câmara, Ricardo
Mobile Robotics
Object Detection
Edge AI
Systematic Review
Embedded Vision
<p>This Zenodo deposit provides the extraction spreadsheet and derived coded dataset used in a systematic literature review (SLR) on real-time object detection for ground (terrestrial) mobile robots, covering primary studies published from 2022 to 2025. The dataset compiles bibliographic metadata and structured variables extracted from the selected papers, including task/application context, sensing modality, deployment locus (e.g., onboard/edge), hardware platform, detector family/model information, and evaluation/reporting attributes.</p> <p>The spreadsheet also preserves “not reported” cases to reflect the original reporting in the primary studies and to support transparency in aggregation. A column-by-column data dictionary is included to document field definitions and coding conventions, enabling reuse and independent verification of the quantitative summaries reported in the paper.</p>
title Extraction Spreadsheet for a Systematic Literature Review on Real-Time Object Detection in Ground Mobile Robots (2022–2025)
topic Mobile Robotics
Object Detection
Edge AI
Systematic Review
Embedded Vision
url https://doi.org/10.5281/zenodo.18511416