Vision-Based SLAM System for Unmanned Aerial Vehicles
The present paper describes a vision-based simultaneous localization and mapping system to be applied to Unmanned Aerial Vehicles (UAVs). The main contribution of this work is to propose a novel estimator relying on an Extended Kalman Filter. The estimator is designed in order to fuse the measuremen...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/3/372 |
| _version_ | 1828365235457622016 |
|---|---|
| author | Rodrigo Munguía Sarquis Urzua Yolanda Bolea Antoni Grau |
| author_facet | Rodrigo Munguía Sarquis Urzua Yolanda Bolea Antoni Grau |
| author_sort | Rodrigo Munguía |
| collection | DOAJ |
| description | The present paper describes a vision-based simultaneous localization and mapping system to be applied to Unmanned Aerial Vehicles (UAVs). The main contribution of this work is to propose a novel estimator relying on an Extended Kalman Filter. The estimator is designed in order to fuse the measurements obtained from: (i) an orientation sensor (AHRS); (ii) a position sensor (GPS); and (iii) a monocular camera. The estimated state consists of the full state of the vehicle: position and orientation and their first derivatives, as well as the location of the landmarks observed by the camera. The position sensor will be used only during the initialization period in order to recover the metric scale of the world. Afterwards, the estimated map of landmarks will be used to perform a fully vision-based navigation when the position sensor is not available. Experimental results obtained with simulations and real data show the benefits of the inclusion of camera measurements into the system. In this sense the estimation of the trajectory of the vehicle is considerably improved, compared with the estimates obtained using only the measurements from the position sensor, which are commonly low-rated and highly noisy. |
| first_indexed | 2024-04-14T05:28:15Z |
| format | Article |
| id | doaj.art-76828559de654811b81a51a184e453ab |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T05:28:15Z |
| publishDate | 2016-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-76828559de654811b81a51a184e453ab2022-12-22T02:09:55ZengMDPI AGSensors1424-82202016-03-0116337210.3390/s16030372s16030372Vision-Based SLAM System for Unmanned Aerial VehiclesRodrigo Munguía0Sarquis Urzua1Yolanda Bolea2Antoni Grau3Department of Automatic Control, Technical University of Catalonia UPC, Barcelona 08036, SpainDepartment of Computer Science, CUCEI, University of Guadalajara, Guadalajara 44430, MexicoDepartment of Automatic Control, Technical University of Catalonia UPC, Barcelona 08036, SpainDepartment of Automatic Control, Technical University of Catalonia UPC, Barcelona 08036, SpainThe present paper describes a vision-based simultaneous localization and mapping system to be applied to Unmanned Aerial Vehicles (UAVs). The main contribution of this work is to propose a novel estimator relying on an Extended Kalman Filter. The estimator is designed in order to fuse the measurements obtained from: (i) an orientation sensor (AHRS); (ii) a position sensor (GPS); and (iii) a monocular camera. The estimated state consists of the full state of the vehicle: position and orientation and their first derivatives, as well as the location of the landmarks observed by the camera. The position sensor will be used only during the initialization period in order to recover the metric scale of the world. Afterwards, the estimated map of landmarks will be used to perform a fully vision-based navigation when the position sensor is not available. Experimental results obtained with simulations and real data show the benefits of the inclusion of camera measurements into the system. In this sense the estimation of the trajectory of the vehicle is considerably improved, compared with the estimates obtained using only the measurements from the position sensor, which are commonly low-rated and highly noisy.http://www.mdpi.com/1424-8220/16/3/372state estimationunmanned aerial vehiclemonocular visionlocalizationmapping |
| spellingShingle | Rodrigo Munguía Sarquis Urzua Yolanda Bolea Antoni Grau Vision-Based SLAM System for Unmanned Aerial Vehicles Sensors state estimation unmanned aerial vehicle monocular vision localization mapping |
| title | Vision-Based SLAM System for Unmanned Aerial Vehicles |
| title_full | Vision-Based SLAM System for Unmanned Aerial Vehicles |
| title_fullStr | Vision-Based SLAM System for Unmanned Aerial Vehicles |
| title_full_unstemmed | Vision-Based SLAM System for Unmanned Aerial Vehicles |
| title_short | Vision-Based SLAM System for Unmanned Aerial Vehicles |
| title_sort | vision based slam system for unmanned aerial vehicles |
| topic | state estimation unmanned aerial vehicle monocular vision localization mapping |
| url | http://www.mdpi.com/1424-8220/16/3/372 |
| work_keys_str_mv | AT rodrigomunguia visionbasedslamsystemforunmannedaerialvehicles AT sarquisurzua visionbasedslamsystemforunmannedaerialvehicles AT yolandabolea visionbasedslamsystemforunmannedaerialvehicles AT antonigrau visionbasedslamsystemforunmannedaerialvehicles |