Vision for Robotics Lab

Open Opportunities

Label transfer from satellite to aerial imagery ETH Zurich Autonomous Systems Lab Other organizations: Vision for Robotics Lab The project aims to implement a semantic label transfer from satellite to aerial imagery in order to enable the training of image-based machine learning algorithms for autonomous aerial vehicle tasks, such as path planning, collision avoidance, and localization. Computer Vision, Intelligent Robotics Semester Project

Long-Term Aerial Localization in Agricultural Environment ETH Zurich Vision for Robotics Lab 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. 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. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time. Computer Vision, Intelligent Robotics, Robotics and Mechatronics Master Thesis

Probabilistic Air-ground Localization in Agricultural Environment ETH Zurich Vision for Robotics Lab 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. Agricultural Engineering, Computer Vision, Intelligent Robotics Master Thesis, Semester Project

Adaptive Motion Parametrization for Continuous-Time Simultaneous-Localization and Mapping ETH Zurich Vision for Robotics Lab Based on our recent works, in this thesis, we aim to investigate different methods to allow more adaptive/dynamic placement of motion-parameterizing control points to address open questions concerning the computation overhead of continuous-time representations. We are looking forward to individually discussing the available theses in greater detail in person. Computer Graphics, Intelligent Robotics Master Thesis, Semester Project

Continuous-Time Simultaneous-Localization and Mapping ETH Zurich Vision for Robotics Lab The emerging paradigm of Continuous-Time Simultaneous Localization And Mapping (CTSLAM) has become a competitive alternative to conventional discrete-time approaches in recent times and holds the additional promise of fusing multi-modal sensor setups in a truly generic manner, rendering its importance to robotic navigation and manipulation seminal. Based on our recent works, there are several possible and interesting extensions that are currently under consideration as student theses, spanning from research-oriented to engineering-oriented topics. We are looking forward to individually discussing the available theses in greater detail in person. Computer Software, Computer Vision, Intelligent Robotics Master Thesis, Semester Project

3D Reconstruction of Natural Environments for Robotic Navigation ETH Zurich Autonomous Systems Lab Other organizations: Vision for Robotics Lab In this project, the student will explore efficient ways of modeling a natural environment to support long-term robotic navigation. Computer Vision, Intelligent Robotics Master Thesis, Semester Project

Content-based Long-term Visual Localization for Aerial Navigation ETH Zurich Vision for Robotics Lab This project proposes to combine both a visual localization pipeline and style transfer techniques to diminish the effects of appearance changes that occur over long periods of time (e.g. seasons, illumination). Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition Master Thesis, Semester Project

Deep-Learning-based Long-Term Aerial Localization in Natural Environment ETH Zurich Autonomous Systems Lab Other organizations: Vision for Robotics Lab 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. 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. The goal of this project is to develop 3D Reconstruction and localization strategies that are capable to identify temporal invariant areas and properties in crop environments during the production season. The main target is to be able to match the same plants over time. Computer Vision, Intelligent Robotics, Robotics and Mechatronics Master Thesis, Semester Project

Neural Radiance Field (NeRF) Based Sensor Fusion in Continuous Time ETH Zurich Autonomous Systems Lab Other organizations: Vision for Robotics Lab In this project we want to investigate the potential of combining a NeRF based mapping approach with a continuous time trajectory representation for sensor fusion. Artificial Intelligence and Signal and Image Processing Master Thesis