Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction
Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/22/6536 |
| _version_ | 1797547797976711168 |
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| author | Cheng-Wei Peng Chen-Chien Hsu Wei-Yen Wang |
| author_facet | Cheng-Wei Peng Chen-Chien Hsu Wei-Yen Wang |
| author_sort | Cheng-Wei Peng |
| collection | DOAJ |
| description | Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage. |
| first_indexed | 2024-03-10T14:50:18Z |
| format | Article |
| id | doaj.art-f2e13aeb1dfc4010afa461588783c672 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:50:18Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f2e13aeb1dfc4010afa461588783c6722023-11-20T21:05:22ZengMDPI AGSensors1424-82202020-11-012022653610.3390/s20226536Cost Effective Mobile Mapping System for Color Point Cloud ReconstructionCheng-Wei Peng0Chen-Chien Hsu1Wei-Yen Wang2Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, TaiwanDepartment of Electrical Engineering, National Taiwan Normal University, Taipei 106, TaiwanDepartment of Electrical Engineering, National Taiwan Normal University, Taipei 106, TaiwanSurvey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage.https://www.mdpi.com/1424-8220/20/22/6536HD mapcolor point cloudmobile mapping system (MMS)autonomous driving |
| spellingShingle | Cheng-Wei Peng Chen-Chien Hsu Wei-Yen Wang Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction Sensors HD map color point cloud mobile mapping system (MMS) autonomous driving |
| title | Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction |
| title_full | Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction |
| title_fullStr | Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction |
| title_full_unstemmed | Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction |
| title_short | Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction |
| title_sort | cost effective mobile mapping system for color point cloud reconstruction |
| topic | HD map color point cloud mobile mapping system (MMS) autonomous driving |
| url | https://www.mdpi.com/1424-8220/20/22/6536 |
| work_keys_str_mv | AT chengweipeng costeffectivemobilemappingsystemforcolorpointcloudreconstruction AT chenchienhsu costeffectivemobilemappingsystemforcolorpointcloudreconstruction AT weiyenwang costeffectivemobilemappingsystemforcolorpointcloudreconstruction |