PL-RAS: A Robust Localization System with Real Time Protection Level Calculation and Adaptive Kernel for Enhanced Integrity
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| Titel: | PL-RAS: A Robust Localization System with Real Time Protection Level Calculation and Adaptive Kernel for Enhanced Integrity |
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| Autoren: | Maharmeh, Elias, Alsayed, Zayed, Nashashibi, Fawzi |
| Weitere Verfasser: | MAHARMEH, Elias |
| Quelle: | 2025 IEEE Intelligent Vehicles Symposium (IV). :427-433 |
| Verlagsinformationen: | IEEE, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | LiDAR baed localzaition, Integrity, Protection Level, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] |
| Beschreibung: | Uncertainty in perception tasks, such as localization, is critical for autonomous systems. Many localization systems fail to ensure that their reported uncertainties encompass the true pose. This paper addresses this issue using the integrity framework. We focus on two main aspects. First, fault-tolerant localization through qualitative evaluation. Second, quantitative estimation of error bounds using (horizontal) protection levels. We introduce PL-RAS (Protection Level-based Robust and Adaptive Solver). This solver aids robustness in non-linear least squares optimization, including factor graph-based localization systems. PL-RAS improves uncertainty awareness and enhances system integrity. It strengthens both qualitative and quantitative integrity aspects. We test the approach on urban road data collected using an acquisition vehicle at Valeo's Créteil VMTC site. The results confirm PL-RAS's effectiveness. In one dataset, the integrity risks are 4.0 × 10 -4 (lateral) and 34.0 × 10 -3 (longitudinal). In a more challenging dataset, the lateral risk becomes 3.0 × 10 -4 , while the longitudinal risk increases to 92.3 × 10 -3 . These findings demonstrate PL-RAS's robustness in fault tolerance and protection level estimation. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/iv64158.2025.11097747 |
| Zugangs-URL: | https://hal.science/hal-05044097v1 |
| Rights: | STM Policy #29 |
| Dokumentencode: | edsair.doi.dedup.....fce422486c9062cdfaa91e1a9a77b560 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Uncertainty in perception tasks, such as localization, is critical for autonomous systems. Many localization systems fail to ensure that their reported uncertainties encompass the true pose. This paper addresses this issue using the integrity framework. We focus on two main aspects. First, fault-tolerant localization through qualitative evaluation. Second, quantitative estimation of error bounds using (horizontal) protection levels. We introduce PL-RAS (Protection Level-based Robust and Adaptive Solver). This solver aids robustness in non-linear least squares optimization, including factor graph-based localization systems. PL-RAS improves uncertainty awareness and enhances system integrity. It strengthens both qualitative and quantitative integrity aspects. We test the approach on urban road data collected using an acquisition vehicle at Valeo's Créteil VMTC site. The results confirm PL-RAS's effectiveness. In one dataset, the integrity risks are 4.0 × 10 -4 (lateral) and 34.0 × 10 -3 (longitudinal). In a more challenging dataset, the lateral risk becomes 3.0 × 10 -4 , while the longitudinal risk increases to 92.3 × 10 -3 . These findings demonstrate PL-RAS's robustness in fault tolerance and protection level estimation. |
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| DOI: | 10.1109/iv64158.2025.11097747 |