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Forest Terrain Object Detection Based on RGB and Depth Information
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. (Machine Design)ORCID iD: 0000-0001-9965-6955
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. (Machine Design)ORCID iD: 0000-0002-9862-828x
2021 (English)In: Proceedings of the 20th International Conference and 9th Americas Conference of the International Society for Terrain-Vehicle Systems / [ed] Martelli M.; Kovecses J.; Shenvi M.; Dixon J., International Society for Terrain-Vehicle Systems , 2021Conference paper, Published paper (Refereed)
Abstract [en]

At present, there is extensive ongoing research and development on autonomous road vehicles, but much less effort is put on autonomous driving in off-road environments, especially with regard to autonomous machines driving in forest terrain. At the same time, forest machine work is harsh for the operator, and the operator’s maximum performance is a major bottleneck for machine operations. One way to enable a safe work environment is to remove the driver from the machine. Thus, forestry machines must become autonomous in order to improve personnel safety and work efficiency. The basis of enabling automation of forest machinery is highly precise and accurate acquisition of working environment information, especially reliable virtual representation of forest terrain and obstacles within. In this paper, the objective is to improve the accuracy of object detection in forest terrain based on an AI algorithm by adding the depth variation texture to corresponding area of  RGB images. The results show that the algorithm significantly improves the accuracy of object detection while the probability of false detection is reduced. Meanwhile, computational efficiency compared to previous image processing is not reduced to such extent that it prevents real-time computation.

Place, publisher, year, edition, pages
International Society for Terrain-Vehicle Systems , 2021.
Keywords [en]
Forest Machine, Stump Detection, Machine Learning, Stereo Camera
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:ltu:diva-83698Scopus ID: 2-s2.0-85124530071OAI: oai:DiVA.org:ltu-83698DiVA, id: diva2:1544566
Conference
20th International Conference and 9th Americas Conference of the International Society for Terrain-Vehicle Systems (ISTVS 2021), [Online], September 27-29, 2021
Funder
Interreg NordVinnovaEU, Horizon 2020, 869580Available from: 2021-04-15 Created: 2021-04-15 Last updated: 2023-10-27Bibliographically approved
In thesis
1. Object Identification for Autonomous Forest Operations
Open this publication in new window or tab >>Object Identification for Autonomous Forest Operations
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Machine Design
Identifiers
urn:nbn:se:ltu:diva-90263 (URN)978-91-8048-077-2 (ISBN)978-91-8048-078-9 (ISBN)
Presentation
2022-06-16, E632, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2022-04-21 Created: 2022-04-20 Last updated: 2023-09-05Bibliographically approved
2. Vision based perception systems for unmanned forestry machines
Open this publication in new window or tab >>Vision based perception systems for unmanned forestry machines
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

After years of continuous development in the past decades, the field of forest production has achieved a notable degree of mechanization. Nonetheless, the unceasing demand for increased productivity in forestry operations continues to drive the rapid expansion of forestry automation. Critical aspects of forest production, including activities such as harvesting, forwarding, seedling planting, and soil preparation, hold significant potential for improved automation. These advancements promise numerous advantages, such as heightened production efficiency, reduced environmental impact, reduced operational costs, and the creation of a more favourable work environment. Beyond fostering industrial progress, such innovations could also make a positive contribution to the sustainable development of forestry.

Forestry operations take place in intricate natural forest environments, rendering the automation of these processes complex and challenging. To realize full automation of forest machines, it is imperative to equip them with environmental awareness and cognitive capabilities. This can be achieved by integrating advanced imaging sensors with sophisticated algorithms, thereby enabling forest machinery to possess a vision system. By utilizing both novel and existing solutions for object detection, positioning, categorization, and status analysis within the machine's surroundings, combined with intelligent decision-making and control mechanisms, forest machinery can attain a higher degree of operational automation.

This thesis primarily focuses on the development, deployment, and validation of vision systems on an unmanned forestry machine platform, with a special emphasis on the prototype development of essential features that are still manually executed in contemporary forestry operations. These features include forest terrain obstacle detection, roundwood pose estimation, automated selection of seedling planting locations, and autonomous obstacle avoidance for mounders.

In this thesis, vision systems rooted in color and stereo camera sensing are designed and deployed on an unmanned forest machinery platform. These systems encompass the following key functionalities:

•             A precise positioning system for detecting forest terrain obstacles, such as stones and stumps, using stereo camera data in conjunction with deep learning techniques.

•             Localization and pose estimation capabilities for ground logs, leveraging stereo cameras and deep learning.

•             An analysis of the planning area for obstacle detection, along with the extraction of feasible planting zones for establishing seedling planting locations.

•             The development of an automated obstacle avoidance system for forestry mounders, powered by visual solutions.

Through rigorous testing in real-world scenarios spanning logging, loading, planting, and site preparation, this paper demonstrates the feasibility and practicality of enhancing the level of automation in forest machinery operations through the integration of vision systems. It culminates in the creation of tangible and efficient functional prototypes, leading towards a new era of automation in the field of forestry.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Vehicle Engineering
Research subject
Machine Design
Identifiers
urn:nbn:se:ltu:diva-101829 (URN)978-91-8048-420-6 (ISBN)978-91-8048-421-3 (ISBN)
Public defence
2024-01-11, E632, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-10-30 Created: 2023-10-27 Last updated: 2023-12-08Bibliographically approved

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Li, SongyuLideskog, Håkan

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