Tremblay2019

Référence

Tremblay, J.-F. (2019) Forest inventory with a lidar-equipped robot for difficult environments. Mémoire de maîtrise, Université Laval. (URL )

Résumé

Forestry is a major industry in many parts of the world. It relies on forest inventory, which consists of measuring tree attributes. In this thesis, we propose the use of 3D mapping, based on the iterative closest point algorithm, to automatically measure tree diameters in forests from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new varied dataset of four different forest sites, 11 trajectories, totalling 1458 tree observations and 1.4 hectares. We provide recommendations for the deployment of mobile robots in a forestry context. We conclude that our mapping method is usable in the context of automated forest inventory, with our best method yielding a root mean square error of 3:45 cm for our whole dataset, and 2:04 cm in ideal conditions consisting of mature forest with well spaced trees.

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@MASTERSTHESIS { Tremblay2019,
    TITLE = { Forest inventory with a lidar-equipped robot for difficult environments },
    AUTHOR = { Tremblay, J.-F. },
    SCHOOL = { Université Laval },
    YEAR = { 2019 },
    NOTE = { CEFTMS, Beland, M. },
    ABSTRACT = { Forestry is a major industry in many parts of the world. It relies on forest inventory, which consists of measuring tree attributes. In this thesis, we propose the use of 3D mapping, based on the iterative closest point algorithm, to automatically measure tree diameters in forests from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new varied dataset of four different forest sites, 11 trajectories, totalling 1458 tree observations and 1.4 hectares. We provide recommendations for the deployment of mobile robots in a forestry context. We conclude that our mapping method is usable in the context of automated forest inventory, with our best method yielding a root mean square error of 3:45 cm for our whole dataset, and 2:04 cm in ideal conditions consisting of mature forest with well spaced trees. },
    URL = { https://corpus.ulaval.ca/jspui/handle/20.500.11794/37634 },
    TIMESTAMP = { 2020-01-09 },
}

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