Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks
Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile tra...
| Main Authors: | , , |
|---|---|
| Format: | Online |
| Language: | eng spa |
| Published: |
Universidad Pedagógica y Tecnológica de Colombia
2022
|
| Subjects: | |
| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14213 |
| _version_ | 1801706007290707968 |
|---|---|
| author | Posso-Bautista, Breyner Bacca-Cortés, Eval Bladimir Caicedo-Bravo, Eduardo |
| author_facet | Posso-Bautista, Breyner Bacca-Cortés, Eval Bladimir Caicedo-Bravo, Eduardo |
| author_sort | Posso-Bautista, Breyner |
| collection | OJS |
| description | Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-14213 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | eng spa |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-142132023-01-31T00:31:29Z Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks Posso-Bautista, Breyner Bacca-Cortés, Eval Bladimir Caicedo-Bravo, Eduardo autonomous vehicles robot localization Kalman filters laser radar vehículos autónomos localización de robots filtros de Kalman radar láser Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks. Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks. Universidad Pedagógica y Tecnológica de Colombia 2022-02-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14213 10.19053/20278306.v12.n1.2022.14213 Revista de Investigación, Desarrollo e Innovación; Vol. 12 No. 1 (2022): Enero-Junio; 121-136 Revista de Investigación, Desarrollo e Innovación; Vol. 12 Núm. 1 (2022): Enero-Junio; 121-136 2389-9417 2027-8306 eng spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14213/11648 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14213/12563 Derechos de autor 2022 Revista de Investigación, Desarrollo e Innovación |
| spellingShingle | autonomous vehicles robot localization Kalman filters laser radar vehículos autónomos localización de robots filtros de Kalman radar láser Posso-Bautista, Breyner Bacca-Cortés, Eval Bladimir Caicedo-Bravo, Eduardo Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_alt | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_full | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_fullStr | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_full_unstemmed | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_short | Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks |
| title_sort | autonomous vehicle localization method based on an extended kalman filter and geo referenced landmarks |
| topic | autonomous vehicles robot localization Kalman filters laser radar vehículos autónomos localización de robots filtros de Kalman radar láser |
| topic_facet | autonomous vehicles robot localization Kalman filters laser radar vehículos autónomos localización de robots filtros de Kalman radar láser |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/14213 |
| work_keys_str_mv | AT possobautistabreyner autonomousvehiclelocalizationmethodbasedonanextendedkalmanfilterandgeoreferencedlandmarks AT baccacortesevalbladimir autonomousvehiclelocalizationmethodbasedonanextendedkalmanfilterandgeoreferencedlandmarks AT caicedobravoeduardo autonomousvehiclelocalizationmethodbasedonanextendedkalmanfilterandgeoreferencedlandmarks |
