Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments
In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. T...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/4/939 |
| _version_ | 1818013427103170560 |
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| author | Safa Ouerghi Rémi Boutteau Xavier Savatier Fethi Tlili |
| author_facet | Safa Ouerghi Rémi Boutteau Xavier Savatier Fethi Tlili |
| author_sort | Safa Ouerghi |
| collection | DOAJ |
| description | In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context. |
| first_indexed | 2024-04-14T06:33:01Z |
| format | Article |
| id | doaj.art-df931bdfe27c41d7b9e0b08548fd26ca |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:33:01Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-df931bdfe27c41d7b9e0b08548fd26ca2022-12-22T02:07:34ZengMDPI AGSensors1424-82202018-03-0118493910.3390/s18040939s18040939Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban EnvironmentsSafa Ouerghi0Rémi Boutteau1Xavier Savatier2Fethi Tlili3Carthage University, SUP’COM, GRESCOM, El Ghazela 2083, TunisiaNormandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, FranceNormandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, FranceCarthage University, SUP’COM, GRESCOM, El Ghazela 2083, TunisiaIn this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context.http://www.mdpi.com/1424-8220/18/4/939real-time navigationvisual-odometrySeqSLAMloop-closureEKFUKF |
| spellingShingle | Safa Ouerghi Rémi Boutteau Xavier Savatier Fethi Tlili Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments Sensors real-time navigation visual-odometry SeqSLAM loop-closure EKF UKF |
| title | Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments |
| title_full | Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments |
| title_fullStr | Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments |
| title_full_unstemmed | Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments |
| title_short | Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments |
| title_sort | visual odometry and place recognition fusion for vehicle position tracking in urban environments |
| topic | real-time navigation visual-odometry SeqSLAM loop-closure EKF UKF |
| url | http://www.mdpi.com/1424-8220/18/4/939 |
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