Under canopy light detection and ranging–based autonomous navigation
Abstract
This study describes a light detection and ranging (LiDAR)–based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM–30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row–based and high–leaf–cover semistructured crop plantations, such as corn (Zea mays) and sorghum (Sorghum bicolor). The key contribution of our work is a LiDAR–based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low–hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within–row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row–lines based on current and previous information. The proposed LiDAR–based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robotmore »
- Authors:
-
- Univ. of Sao Paulo, Sao Paulo (Brazil)
- Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
- Publication Date:
- Research Org.:
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- OSTI Identifier:
- 1489516
- Alternate Identifier(s):
- OSTI ID: 1485530
- Grant/Contract Number:
- AR0000598
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Journal of Field Robotics
- Additional Journal Information:
- Journal Volume: 36; Journal Issue: 3; Journal ID: ISSN 1556-4959
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; agriculture; perception; terrestrial robotics
Citation Formats
Higuti, Vitor A. H., Velasquez, Andres E. B., Magalhaes, Daniel Varela, Becker, Marcelo, and Chowdhary, Girish. Under canopy light detection and ranging–based autonomous navigation. United States: N. p., 2018.
Web. doi:10.1002/rob.21852.
Higuti, Vitor A. H., Velasquez, Andres E. B., Magalhaes, Daniel Varela, Becker, Marcelo, & Chowdhary, Girish. Under canopy light detection and ranging–based autonomous navigation. United States. https://doi.org/10.1002/rob.21852
Higuti, Vitor A. H., Velasquez, Andres E. B., Magalhaes, Daniel Varela, Becker, Marcelo, and Chowdhary, Girish. 2018.
"Under canopy light detection and ranging–based autonomous navigation". United States. https://doi.org/10.1002/rob.21852. https://www.osti.gov/servlets/purl/1489516.
@article{osti_1489516,
title = {Under canopy light detection and ranging–based autonomous navigation},
author = {Higuti, Vitor A. H. and Velasquez, Andres E. B. and Magalhaes, Daniel Varela and Becker, Marcelo and Chowdhary, Girish},
abstractNote = {This study describes a light detection and ranging (LiDAR)–based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM–30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row–based and high–leaf–cover semistructured crop plantations, such as corn (Zea mays) and sorghum (Sorghum bicolor). The key contribution of our work is a LiDAR–based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low–hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within–row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row–lines based on current and previous information. The proposed LiDAR–based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR–based navigation in realistic field environments for small under–canopy robots.},
doi = {10.1002/rob.21852},
url = {https://www.osti.gov/biblio/1489516},
journal = {Journal of Field Robotics},
issn = {1556-4959},
number = 3,
volume = 36,
place = {United States},
year = {Wed Dec 12 00:00:00 EST 2018},
month = {Wed Dec 12 00:00:00 EST 2018}
}
Web of Science
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