AN EVALUATION OF SOLID-STATE LIDAR FOR LOCALIZATION AND HD POINT CLOUD MAPPING

Cost-effective navigation and positioning systems for autonomous vehicles has become a key focus of research in recent years. Having an accurate position within a lane is vital to enabling high levels of automation and improving safety. Traditionally, vehicle navigation and positioning systems have...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1/W2-2023; pp. 841 - 847
Main Authors Lu, Y.-E., Chiang, K.-W., Tsai, M.-L., Chiu, Y.-T., Srinara, S., Wu, T.-C., El-Sheimy, N.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 01.01.2023
Copernicus Publications
Subjects
Autonomous cars
Canyons
Commercial vehicles
Global navigation satellite system
Lidar
Localization
Mapping
Mass production
Multisensor fusion
Navigation
Navigational satellites
Positioning systems
Sensors
Simultaneous localization and mapping
Solid state
Street canyons
System effectiveness
Vehicles
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Summary:Cost-effective navigation and positioning systems for autonomous vehicles has become a key focus of research in recent years. Having an accurate position within a lane is vital to enabling high levels of automation and improving safety. Traditionally, vehicle navigation and positioning systems have relied heavily on the Global Navigation Satellite System (GNSS), particularly in open-sky scenarios. However, GNSS signals can be easily disrupted by environmental interferences. These include phenomena such as urban canyons, which result from multi-path interferences, as well as challenges posed by Non-Line-of-Sight (NLOS) situations. In the pursuit of developing robust systems resilient to such issues, the concept of sensor fusion has been widely employed. Among all sensors used in commercial self-driving vehicles, mechanical LiDAR is the primary sensor. Utilizing point cloud data from LiDAR and registering it with a prior point cloud map can result in highly accurate position results. However, the high cost of mechanical LiDAR has limited the mass production of point cloud map and autonomous vehicle. In this paper, we evaluate several successful Simultaneous Localization and Mapping (SLAM) architectures from LiDAR-based to LiDAR-Inertial-based using single Solid-State LiDAR (SSL). Last, we proposed a single SSL mapping and localization framework that can achieve 36 centimeters 3D RMSE and 0.5 degree accuracy in heading estimation.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLVIII-1-W2-2023-841-2023