Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization
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 tha...
| Päätekijät: | , , , , |
|---|---|
| Aineistotyyppi: | info:eu-repo/semantics/article |
| Kieli: | English |
| Julkaistu: |
MDPI
2020
|
| Aiheet: | |
| Linkit: | http://hdl.handle.net/10835/7556 |
| _version_ | 1789406731279794176 |
|---|---|
| author | Blanco Claraco, José Luis Mañas Alvarez, Francisco Torres Moreno, José Luis Rodríguez Díaz, Francisco Giménez Fernández, Antonio |
| author_facet | Blanco Claraco, José Luis Mañas Alvarez, Francisco Torres Moreno, José Luis Rodríguez Díaz, Francisco Giménez Fernández, Antonio |
| author_sort | Blanco Claraco, José Luis |
| collection | DSpace |
| description | 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 ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼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 | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7556 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-75562023-04-12T19:33:35Z Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization Blanco Claraco, José Luis Mañas Alvarez, Francisco Torres Moreno, José Luis Rodríguez Díaz, Francisco Giménez Fernández, Antonio global positioning system mobile robots simultaneous localization and mapping particle filter district 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 ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼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. 2020-01-17T12:46:20Z 2020-01-17T12:46:20Z 2019-07-17 info:eu-repo/semantics/article 1424-8220 http://hdl.handle.net/10835/7556 en https://www.mdpi.com/1424-8220/19/14/3155 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | global positioning system mobile robots simultaneous localization and mapping particle filter district Blanco Claraco, José Luis Mañas Alvarez, Francisco Torres Moreno, José Luis Rodríguez Díaz, Francisco Giménez Fernández, Antonio Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title_full | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title_fullStr | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title_full_unstemmed | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title_short | Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization |
| title_sort | benchmarking particle filter algorithms for efficient velodyne-based vehicle localization |
| topic | global positioning system mobile robots simultaneous localization and mapping particle filter district |
| url | http://hdl.handle.net/10835/7556 |
| work_keys_str_mv | AT blancoclaracojoseluis benchmarkingparticlefilteralgorithmsforefficientvelodynebasedvehiclelocalization AT manasalvarezfrancisco benchmarkingparticlefilteralgorithmsforefficientvelodynebasedvehiclelocalization AT torresmorenojoseluis benchmarkingparticlefilteralgorithmsforefficientvelodynebasedvehiclelocalization AT rodriguezdiazfrancisco benchmarkingparticlefilteralgorithmsforefficientvelodynebasedvehiclelocalization AT gimenezfernandezantonio benchmarkingparticlefilteralgorithmsforefficientvelodynebasedvehiclelocalization |