Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)
Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterize...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7947 |
| _version_ | 1797577013125447680 |
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| author | Yangwoo Lee Minsoo Kim Joonwoo Ahn Jaeheung Park |
| author_facet | Yangwoo Lee Minsoo Kim Joonwoo Ahn Jaeheung Park |
| author_sort | Yangwoo Lee |
| collection | DOAJ |
| description | Accurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches. |
| first_indexed | 2024-03-10T22:01:56Z |
| format | Article |
| id | doaj.art-fd4660710ca24dc5af316803eeb06399 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:56Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd4660710ca24dc5af316803eeb063992023-11-19T12:56:29ZengMDPI AGSensors1424-82202023-09-012318794710.3390/s23187947Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP)Yangwoo Lee0Minsoo Kim1Joonwoo Ahn2Jaeheung Park3Dynamic Robotic Systems (DYROS) Lab, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of KoreaDynamic Robotic Systems (DYROS) Lab, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of KoreaSamsung Advanced Institute of Technology, Samsung Electronics, Suwon 16678, Republic of KoreaDynamic Robotic Systems (DYROS) Lab, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of KoreaAccurately estimating the pose of a vehicle is important for autonomous parking. The study of around view monitor (AVM)-based visual Simultaneous Localization and Mapping (SLAM) has gained attention due to its affordability, commercial availability, and suitability for parking scenarios characterized by rapid rotations and back-and-forth movements of the vehicle. In real-world environments, however, the performance of AVM-based visual SLAM is degraded by AVM distortion errors resulting from an inaccurate camera calibration. Therefore, this paper presents an AVM-based visual SLAM for autonomous parking which is robust against AVM distortion errors. A deep learning network is employed to assign weights to parking line features based on the extent of the AVM distortion error. To obtain training data while minimizing human effort, three-dimensional (3D) Light Detection and Ranging (LiDAR) data and official parking lot guidelines are utilized. The output of the trained network model is incorporated into weighted Generalized Iterative Closest Point (GICP) for vehicle localization under distortion error conditions. The experimental results demonstrate that the proposed method reduces localization errors by an average of 39% compared with previous AVM-based visual SLAM approaches.https://www.mdpi.com/1424-8220/23/18/7947visual Simultaneous Localization and Mappingautonomous parkingAVM distortion errordeep learningweighted Generalized Iterative Closest Point |
| spellingShingle | Yangwoo Lee Minsoo Kim Joonwoo Ahn Jaeheung Park Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) Sensors visual Simultaneous Localization and Mapping autonomous parking AVM distortion error deep learning weighted Generalized Iterative Closest Point |
| title | Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) |
| title_full | Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) |
| title_fullStr | Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) |
| title_full_unstemmed | Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) |
| title_short | Accurate Visual Simultaneous Localization and Mapping (SLAM) against Around View Monitor (AVM) Distortion Error Using Weighted Generalized Iterative Closest Point (GICP) |
| title_sort | accurate visual simultaneous localization and mapping slam against around view monitor avm distortion error using weighted generalized iterative closest point gicp |
| topic | visual Simultaneous Localization and Mapping autonomous parking AVM distortion error deep learning weighted Generalized Iterative Closest Point |
| url | https://www.mdpi.com/1424-8220/23/18/7947 |
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