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Benchmarking of Various LiDAR Sensors for Use in Self-Driving Vehicles in Real-World Environments

In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in o...

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Detalles Bibliográficos
Autores principales: Schulte-Tigges, Joschua, Förster, Marco, Nikolovski, Gjorgji, Reke, Michael, Ferrein, Alexander, Kaszner, Daniel, Matheis, Dominik, Walter, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572247/
https://www.ncbi.nlm.nih.gov/pubmed/36236247
http://dx.doi.org/10.3390/s22197146
Descripción
Sumario:In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.