Abstract
Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.
Highlights
An autonomous vehicle (AV) is a self-driving car that has a powerful real-time perception and decision-making system [1]
The georeferencing solution was obtained with the integration of Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (IMU) data
Using a navigation-grade IMU was an essential contribution to achieving a highly accurate and seamless navigation solution, as the cm-level georeferencing accuracy is critical for the point cloud accuracy evaluation, as the ranging accuracy of the Velodyne sensor is in the few cm range
Summary
An autonomous vehicle (AV) is a self-driving car that has a powerful real-time perception and decision-making system [1]. The automotive industry leaders, information and communication technology companies, and researchers aim for fully automated vehicles to participate in the emerging market of autonomous vehicles [3], currently, most commercially available vehicles use advanced driver assistance systems (ADAS) support level 2 and only a few recently introduced vehicles have Level 3 performance. Vehicle-to-roadside-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications help to improve traffic safety and autonomous driving functionality [4]. It is expected that fully automated cars will be commercialized, and will appear on the roads in the coming years [5], will prevent driver-related accidents [6], and will decrease transportation problems such as regulating traffic flow [4].
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