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

Benchmarking of Various LiDAR Sensors for Use in Self-Driving Vehicles in Real-World Environments. Schulte-Tigges, J., Förster, M., Nikolovski, G., Reke, M., Ferrein, A., Kaszner, D., Matheis, D., & Walter, T. Sensors, 2022.

Paper doi abstract bibtex

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.

@Article{Schulte-Tigges-etAl_Sensors2022_Benchmarking-LIDAR-Sensors,
  AUTHOR       = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and
                  Nikolovski, Gjorgji and Reke, Michael and
		  Ferrein, Alexander and Kaszner, Daniel and
		  Matheis, Dominik and Walter, Thomas},
  TITLE        = {Benchmarking of Various LiDAR Sensors for Use in Self-Driving Vehicles in Real-World Environments},
  JOURNAL      = {Sensors},
  VOLUME       = {22},
  YEAR         = {2022},
  NUMBER       = {19},
  ARTICLE-NUMBER = {7146},
  URL          = {https://www.mdpi.com/1424-8220/22/19/7146},
  PubMedID     = {36236247},
  ISSN         = {1424-8220},
  DOI          = {10.3390/s22197146},
  keywords     = {ADP; LiDAR; benchmark; self-driving},
  ABSTRACT     = {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.},
}

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