Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916094422286336 |
|---|---|
| author | Blanco-Claraco, Jose Luis Mañas-Alvarez, Francisco Torres-Moreno, Jose Luis Rodriguez, Francisco Gimenez-Fernandez, Antonio |
| author_facet | Blanco-Claraco, Jose Luis Mañas-Alvarez, Francisco Torres-Moreno, Jose Luis Rodriguez, Francisco Gimenez-Fernandez, Antonio |
| contents | Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of $\sim$2 particles/m$^2$ is required to achieve 100% convergence success for large-scale ($\sim$100,000 m$^2$), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization Blanco-Claraco, Jose Luis Mañas-Alvarez, Francisco Torres-Moreno, Jose Luis Rodriguez, Francisco Gimenez-Fernandez, Antonio Robotics Applications Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of $\sim$2 particles/m$^2$ is required to achieve 100% convergence success for large-scale ($\sim$100,000 m$^2$), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software. |
| title | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| topic | Robotics Applications |
| url | https://arxiv.org/abs/2401.08870 |