Canopy-Based Monte Carlo Localization in Orchards Using Top-View Imagery
| Autor: | Omer Shalev, Amir Degani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Canopy
0209 industrial biotechnology Control and Optimization business.industry Computer science Mechanical Engineering Biomedical Engineering Monte Carlo localization Mobile robot 02 engineering and technology Kidnapped robot problem GeneralLiterature_MISCELLANEOUS Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Precision agriculture Artificial intelligence business |
| Zdroj: | IEEE Robotics and Automation Letters. 5:2403-2410 |
| ISSN: | 2377-3774 |
| Popis: | Localization of ground mobile robots in orchards is a complex problem which is yet to be fully addressed. The typical localization approaches are not adjusted to the characteristics of the orchard environment, especially the homogeneous scenery. To alleviate these difficulties, we propose to use top-view images of the orchard acquired in real-time. The top-view observation of the orchard provides a unique signature of every tree formed by the shape of its canopy. This practically changes the homogeneity premise in orchards and paves the way for addressing the kidnapped robot problem. Using computer vision techniques, we build a virtual canopies laser scan around the ground robot which is generated from low-altitude top-view video streams. We apply Monte Carlo Localization on this virtual scan to localize the robot against a high-altitude top-view snapshot image which is used as a map. The suggested approach is examined in numerous offline experiments conducted on data acquired in real orchards and is compared against a typical simulated approach which relies on ground-level trunk observations. The canopy-based approach demonstrated better performance in all measures, including convergence to centimeter-level accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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