LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 21; číslo 10; s. 3313
Hlavní autoři: Sobczak, Łukasz, Filus, Katarzyna, Domański, Adam, Domańska, Joanna
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 11.05.2021
MDPI
Témata:
Algorithms
Automobiles
autonomous driving
Autonomous vehicles
Cameras
LiDAR
Localization
Performance evaluation
Roads & highways
Robotics
Sensors
Simulation
SLAM
Software
ISSN:1424-8220, 1424-8220
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Shrnutí:With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21103313