3D perception, sensor fusion, mobile autonomy, and long-term autonomy.
Papers and presentations
- Heiwolt, K., Mandil, W., Cielniak, G. and Hanheide, M. (2020). Automated Topological Mapping for Agricultural Robots. UKRAS20 Conference: “Robots into the real world” Proceedings, 27-29. doi: 10.31256/Ze8Ex1V
- Heiwolt, K. (2020). Semantic Segmentation of Plant Leaves from 3D Point Clouds using Deep Learning. Lincoln Conference on Intelligent Robots and Systems 2020, online.
- Heiwolt, K. (2020). Deep 3D semantic segmentation of plant point clouds. Lincoln Agri-Robotics Mini Conference, Dec 2020.
- Heiwolt, K. (2021). Using deep learning for semantic segmentation of 3D point clouds. AgriFoRwArdS CDT Annual Conference 2021, online.
- De Silva, O., Heiwolt, K., Rogers, H., Wang, H., and Wang, N. (2021). RAS Counter: Non-invasive yield prediction for vineyards, AgriFoRwArdS CDT Annual Conference 2021, online.
- Oppermann, L., Hirzel, S., Güldner, Heiwolt, K., Krassowski, J., Schade, U., Lange, C., and Prinz, W. (2021). Finding and analysing energy research funding data: The EnArgus system. Energy and AI, vol. 5. https://doi.org/10.1016/j.egyai.2021.100070
- Heiwolt, K. (2021). Deep semantic segmentation of 3D plant point clouds. Towards Autonomous Robotic Systems 2021, online.
- Member of the AgriFoRwArdS CDT Drink Outside the Box Organisation Committee.
- Member of the AgriFoRwArdS CDT Advisory Board.
- Member of the AgriFoRwArdS CDT Equality, Diversity and Inclusion Panel.
My name is Karoline, I am from Germany, and I joined the CDT in September 2019. I have a background in neuroscience and robotics and I am especially interested in the research areas of 3D perception, sensor fusion, and mobile autonomy. I chose to join this CDT because the development of robots for agricultural applications offers many interesting real-world challenges that could have a great positive impact on a sustainable global food chain. I am currently working on my PhD with Grzegorz Cielniak at the University of Lincoln alongside a brilliant cohort of fellow PhD students with different backgrounds.
Semantic Segmentation of Plant Leaves from 3D Point Clouds using Deep Learning
In this project we aim to address the problem of semantically segmenting plant leaves from the background and other plant organs in three-dimensional point clouds of individual plants captured by RGBD sensors. Previous work utilises explicit prior knowledge about the expected plant morphology and sensor set-up, as well as manually tuned parameters to achieve this segmentation. Here we propose to train a supervised machine learning algorithm to predict the segmentation output directly from point cloud data and minimise the necessary user input.
4D Scene Analysis for Autonomous Operation of Mobile Robots on Farms
This research addresses challenges in 4D scene analysis for autonomous operation of mobile robots on farms. The deployment of agricultural robots will increase sustainability and support precision farming operations tuned to needs of individual plants. This research will enable robots to maintain precise 3D representations of uncertain and highly variable farm environments together with their semantics in order to safely traverse the farm autonomously, as well as to reconstruct structural representations of the crop. Additionally, registering these representations over time would allow for sustained crop monitoring and provide insight into spatio-temporal dynamics and interactions between the plants and environmental factors.