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Probabilistic Air-ground Localization in Agricultural Environment
Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. The goal of this project is to develop air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images.
Keywords: 3d Reconstruction, Place Recognition, Aerial Vehicles, Deep Learning, Computer Vision.
The construction of digital environments is increasingly important in industries like aerospace, automotive, and AEC. Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Furthermore, a digital twin enables decision-making and -evaluation in the virtual world before committing to actions in the real world. Digital environments will play an equally important role for future agriculture. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. An agricultural digital twin differs from an industrial digital twin in need for periodic monitoring of growing crops and conditions on farmland, and in requiring new methods for capturing vegetation geometry and modeling plant growth that exceed the state-of-the-art. One of the main challenges is the fusion of close-range ground imagery with aerial data.
As such, the goal of this project is to develop air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images. For this, the dynamic aspects of the model will have to be taken into consideration. Geometric and Machine learning approaches will be necessary in this project. Simulation and real-drones will be used in the project. The student will have the opportunity to learn all aspects of the research, from the capture of the data to the final localization pipeline. Both perennial and non-perennial crops will be considered in the project. In addition, this work will be carried out in cooperation with an international company.
The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial.
The construction of digital environments is increasingly important in industries like aerospace, automotive, and AEC. Digital environments, or digital twins, allow for design, prototyping, and testing in the virtual world before moving to the real world, thus accelerating development and reducing costs. Furthermore, a digital twin enables decision-making and -evaluation in the virtual world before committing to actions in the real world. Digital environments will play an equally important role for future agriculture. A digital twin of a farm supports crop operations such as scheduling a harvest or predicting a yield, while agritech companies can develop farm automation robots using a digital twin. An agricultural digital twin differs from an industrial digital twin in need for periodic monitoring of growing crops and conditions on farmland, and in requiring new methods for capturing vegetation geometry and modeling plant growth that exceed the state-of-the-art. One of the main challenges is the fusion of close-range ground imagery with aerial data. As such, the goal of this project is to develop air-ground localization strategies that are capable to be deployed in crop environments during the production season. The main target is to identify individual plants in the ground images using as reference the aerial images. For this, the dynamic aspects of the model will have to be taken into consideration. Geometric and Machine learning approaches will be necessary in this project. Simulation and real-drones will be used in the project. The student will have the opportunity to learn all aspects of the research, from the capture of the data to the final localization pipeline. Both perennial and non-perennial crops will be considered in the project. In addition, this work will be carried out in cooperation with an international company. The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial.
- WP1: Literature review of existing state-of-the-art air-ground localization for agricultural environments,
- WP2: Build a simulation-based and real-life dataset using Lab-tools and drones.
- WP3: Development of a localization and reconstruction pipeline capable of selecting the best features to be identified between air and ground images.
- WP4: Evaluation of the proposed method and comparison against state-of-the-art approaches and report writing.
- WP1: Literature review of existing state-of-the-art air-ground localization for agricultural environments, - WP2: Build a simulation-based and real-life dataset using Lab-tools and drones. - WP3: Development of a localization and reconstruction pipeline capable of selecting the best features to be identified between air and ground images. - WP4: Evaluation of the proposed method and comparison against state-of-the-art approaches and report writing.
The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial.
The student taking this project needs to be highly motivated, preferably with strong analytical skills, while experience in coding in C/C++ and Python, Computer Vision, and Deep Learning would be very beneficial.