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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05811 |
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| _version_ | 1866914909470588928 |
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| author | Weishaupt, Fabio Tilly, Julius F. Appenrodt, Nils Fischer, Pascal Dickmann, Jürgen Heberling, Dirk |
| author_facet | Weishaupt, Fabio Tilly, Julius F. Appenrodt, Nils Fischer, Pascal Dickmann, Jürgen Heberling, Dirk |
| contents | Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy of a few centimeters and degrees, respectively. This paper presents a radar-based approach to self-localization, which exploits fully polarimetric scattering information for robust landmark detection. The proposed method requires no input from sensors other than radar during localization for a given map. By association of landmark observations with map landmarks, the vehicle's position is inferred. Abstract point- and line-shaped landmarks allow for compact map sizes and, in combination with the factor graph formulation used, for an efficient implementation. Evaluation of extensive real-world experiments in diverse environments shows a promising overall localization performance of $0.12 \text{m}$ RMS absolute trajectory and $0.43 {}^\circ$ RMS heading error by leveraging the polarimetric information. A comparison of the performance of different levels of polarimetric information proves the advantage in challenging scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05811 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars Weishaupt, Fabio Tilly, Julius F. Appenrodt, Nils Fischer, Pascal Dickmann, Jürgen Heberling, Dirk Robotics Signal Processing Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy of a few centimeters and degrees, respectively. This paper presents a radar-based approach to self-localization, which exploits fully polarimetric scattering information for robust landmark detection. The proposed method requires no input from sensors other than radar during localization for a given map. By association of landmark observations with map landmarks, the vehicle's position is inferred. Abstract point- and line-shaped landmarks allow for compact map sizes and, in combination with the factor graph formulation used, for an efficient implementation. Evaluation of extensive real-world experiments in diverse environments shows a promising overall localization performance of $0.12 \text{m}$ RMS absolute trajectory and $0.43 {}^\circ$ RMS heading error by leveraging the polarimetric information. A comparison of the performance of different levels of polarimetric information proves the advantage in challenging scenarios. |
| title | Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars |
| topic | Robotics Signal Processing |
| url | https://arxiv.org/abs/2408.05811 |