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Velocity Estimation from LiDAR Sensors Motion Distortion Effect

  • Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortionMany modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.show moreshow less

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Metadaten
Author:Lukas HaasORCiDGND, Arsalan HaiderORCiD, Ludwig KastnerORCiD, Thomas ZehORCiDGND, Tim PoguntkeGND, Matthias Kuba, Michael Schardt, Martin Jakobi, Alexander W. Koch
DOI:https://doi.org/10.3390/s23239426
Identifier:1424-8220 OPAC HS OPAC extern
Parent Title (English):Sensors
Publisher:MDPI
Place of publication:Basel, Switzerland
Editor:Federico Angelini
Document Type:Article
Language:English
Date of Publication (online):2023/11/26
Year of first Publication:2023
Tag:LiDAR sensor; advanced driver assistance systems; deep learning; highly automated driving; motion distortion effect; point cloud; velocity estimation
Volume:23.
Issue:23
Article Number:9426
Number of pages:16 Seiten
First Page:1
Last Page:16
Institutes:Fakultät Elektrotechnik
IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP2: Mobilität
FSP4: Soziale Innovationen
Publication Lists:Kuba, Matthias
Poguntke, Tim
Zeh, Thomas
Haider, Arsalan
Haas, Lukas
Kastner, Ludwig
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/01/29
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