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https://doi.org/10.21256/zhaw-29671| Publication type: | Bachelor thesis |
| Title: | Simultaneous localization and mapping algorithm for an autonomous race car |
| Authors: | Sprecher, Andreas Stöckli, Andrea |
| Advisors / Reviewers: | Reif, Monika Ulrike Da Silva Miranda, Luis Miguel |
| DOI: | 10.21256/zhaw-29671 |
| Extent: | 79 |
| Issue Date: | 2023 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Publisher / Ed. Institution: | Winterthur |
| Language: | English |
| Subjects: | Autonomous robots; SLAM; Formula student |
| Subject (DDC): | 629: Aeronautical, automotive engineering |
| Abstract: | The field of autonomous robotics, an emergent discipline within engineering, has attracted substantial interest in recent years. One of the notable platforms that challenge and stimulate progress in this area is the Formula Student driverless competition. This international contest prompts students to engineer, design, and fabricate a formula-style, single-seater race car capable of autonomous driving. The core of these driverless systems relies heavily on Simultaneous Localization and Mapping algorithms (SLAM), making them an essential focus of research and development for all types of autonomous robots. Literature shows a variety of approaches to tackle the SLAM problem. In light of this diversity, we selected the most promising algorithms suitable for our specific use case, considering numerous requirements and restrictions related to the entire driverless platform developed by our team at Zurich UAS Racing. Besides the implementation of EKF SLAM and FastSLAM, specific metrics and test cases were established that allow for their verification and comparative analysis. We further developed supporting code, such as a car simulator and an algorithm evaluator, as well as a software pipeline to automate the build and release processes of the whole driverless system. Our implementation demonstrated that the chosen SLAM algorithms produced satisfactory results by generating maps suitable for navigation. Notably, our experimental results showed FastSLAM outperforming EKF SLAM, thus it was selected as the algorithm of choice for the upcoming driverless competition. The successful implementation highlights how these algorithms, particularly FastSLAM, can play a key role in elevating the performance and reliability of the autonomous driving system at Zurich UAS Racing. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29671 |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Appears in collections: | Bachelorarbeiten ZHAW School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Sprecher-Andreas_Stoeckli-Andrea_BA_SoE.pdf | 3.57 MB | Adobe PDF |  View/Open |
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Sprecher, A., & Stöckli, A. (2023). Simultaneous localization and mapping algorithm for an autonomous race car [Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften]. https://doi.org/10.21256/zhaw-29671
Sprecher, A. and Stöckli, A. (2023) Simultaneous localization and mapping algorithm for an autonomous race car. Bachelor’s thesis. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-29671.
A. Sprecher and A. Stöckli, “Simultaneous localization and mapping algorithm for an autonomous race car,” Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, Winterthur, 2023. doi: 10.21256/zhaw-29671.
SPRECHER, Andreas und Andrea STÖCKLI, 2023. Simultaneous localization and mapping algorithm for an autonomous race car. Bachelor’s thesis. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Sprecher, Andreas, and Andrea Stöckli. 2023. “Simultaneous Localization and Mapping Algorithm for an Autonomous Race Car.” Bachelor’s thesis, Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29671.
Sprecher, Andreas, and Andrea Stöckli. Simultaneous Localization and Mapping Algorithm for an Autonomous Race Car. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, https://doi.org/10.21256/zhaw-29671.
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