The need to further unlock productivity of forestry operations urges the increase of forestry automation. Many essential operations in forest production, such as harvesting, forwarding, planting, etc., have the potential to be automated and obtain benefits such as improved production efficiency, reduced operating costs, and an improved working environment. In view of the fact that forestry operations are performed in forest environments, the automation of forestry operations is thus complex and extremely challenging. To build the ability of forest machine automation, it is necessary to construct an environmental cognitive ability of the forest machine as basis. Through a combination of exteroceptive sensors and algorithms, forest machine vision can be realized. Using new and off-the-shelf solutions for detecting, locating, classifying and analyzing the status of objects of concern surrounding the machine during forestry operations in combination with smart judgement and control, forest operations can be automated. This thesis focuses on the introduction of vision systems on an unmanned forest platform, aiming to create the foundation for autonomous decision-making and execution in forestry operations. Initially, the vision system is designed to work on an unmanned forest machine platform, to create necessary conditions to either assist operators or to realize automatic operation as a further step. In this thesis, vision systems based on stereo camera sensing are designed and deployed on an unmanned forest machine platform and the functions of detection, localization and pose estimation of objects that surround the machine are developed and evaluated. These mainly include a positioning function for forest terrain obstacles such as stones and stumps based on stereo camera data and deep learning, and a localization and pose estimation function for ground logs based on stereo camera and deep learning with added functionality of color difference comparison. By testing these systems’ performance in realistic scenarios, this thesis describe the feasibility of improving the automation level of forest machine operation by building a vision system. In addition, the thesis also demonstrate that the accuracy of stump detection can be improved without significantly increasing the processing load by introducing depth information into training and execution.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-90263 |
| Date | January 2022 |
| Creators | Li, Songyu |
| Publisher | Luleå tekniska universitet, Produkt- och produktionsutveckling, Luleå |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Licentiate thesis, comprehensive summary, info:eu-repo/semantics/masterThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Licentiate thesis / Luleå University of Technology, 1402-1757 |
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