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Localization techniques for drone racing
Benchmark comparison of localization techniques.
Keywords: SLAM, Localization, Drone Racing
For fast and agile flight, most approaches require precise knowledge of the metric state. In contrast to a classical SLAM setting, drone racing offers additional features. In this project, we want to evaluate and compare different strategies for localization in this drone-racing scenario.
The following classes of methods could be investigated:
- classic feature-based SLAM
- learned features with classic SLAM pipeline
- learning-based localization
- filtering based approaches
Requirements:
- Machine learning experience (TensorFlow and/or PyTorch)
- Programming experience in C++ and Python
For fast and agile flight, most approaches require precise knowledge of the metric state. In contrast to a classical SLAM setting, drone racing offers additional features. In this project, we want to evaluate and compare different strategies for localization in this drone-racing scenario.
The following classes of methods could be investigated: - classic feature-based SLAM - learned features with classic SLAM pipeline - learning-based localization - filtering based approaches
Requirements: - Machine learning experience (TensorFlow and/or PyTorch) - Programming experience in C++ and Python
The goal of the project is to gain a detailed understanding of which method is best suited for
- real-time localization and
- offline postprocessing
of the data.
The goal of the project is to gain a detailed understanding of which method is best suited for - real-time localization and - offline postprocessing of the data.
Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Giovanni Cioffi ( cioffi (at) ifi (dot) uzh (dot) ch)
Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Giovanni Cioffi ( cioffi (at) ifi (dot) uzh (dot) ch)