Consistent Pose Estimation of Unmanned Ground Vehicles through Terrain-Aided Multi-Sensor Fusion on Geometric Manifolds
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912853840101376 |
|---|---|
| author | Raab, Alexander Weiss, Stephan Fornasier, Alessandro Brommer, Christian Ibrahim, Abdalrahman |
| author_facet | Raab, Alexander Weiss, Stephan Fornasier, Alessandro Brommer, Christian Ibrahim, Abdalrahman |
| contents | Aiming to enhance the consistency and thus long-term accuracy of Extended Kalman Filters for terrestrial vehicle localization, this paper introduces the Manifold Error State Extended Kalman Filter (M-ESEKF). By representing the robot's pose in a space with reduced dimensionality, the approach ensures feasible estimates on generic smooth surfaces, without introducing artificial constraints or simplifications that may degrade a filter's performance. The accompanying measurement models are compatible with common loosely- and tightly-coupled sensor modalities and also implicitly account for the ground geometry. We extend the formulation by introducing a novel correction scheme that embeds additional domain knowledge into the sensor data, giving more accurate uncertainty approximations and further enhancing filter consistency. The proposed estimator is seamlessly integrated into a validated modular state estimation framework, demonstrating compatibility with existing implementations. Extensive Monte Carlo simulations across diverse scenarios and dynamic sensor configurations show that the M-ESEKF outperforms classical filter formulations in terms of consistency and stability. Moreover, it eliminates the need for scenario-specific parameter tuning, enabling its application in a variety of real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consistent Pose Estimation of Unmanned Ground Vehicles through Terrain-Aided Multi-Sensor Fusion on Geometric Manifolds Raab, Alexander Weiss, Stephan Fornasier, Alessandro Brommer, Christian Ibrahim, Abdalrahman Robotics Aiming to enhance the consistency and thus long-term accuracy of Extended Kalman Filters for terrestrial vehicle localization, this paper introduces the Manifold Error State Extended Kalman Filter (M-ESEKF). By representing the robot's pose in a space with reduced dimensionality, the approach ensures feasible estimates on generic smooth surfaces, without introducing artificial constraints or simplifications that may degrade a filter's performance. The accompanying measurement models are compatible with common loosely- and tightly-coupled sensor modalities and also implicitly account for the ground geometry. We extend the formulation by introducing a novel correction scheme that embeds additional domain knowledge into the sensor data, giving more accurate uncertainty approximations and further enhancing filter consistency. The proposed estimator is seamlessly integrated into a validated modular state estimation framework, demonstrating compatibility with existing implementations. Extensive Monte Carlo simulations across diverse scenarios and dynamic sensor configurations show that the M-ESEKF outperforms classical filter formulations in terms of consistency and stability. Moreover, it eliminates the need for scenario-specific parameter tuning, enabling its application in a variety of real-world settings. |
| title | Consistent Pose Estimation of Unmanned Ground Vehicles through Terrain-Aided Multi-Sensor Fusion on Geometric Manifolds |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.14661 |