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Velocity Estimation from LiDAR Sensors Motion Distortion Effect.

Lukas Haas1,2, Arsalan Haider1,2, Ludwig Kastner1

  • 1IFM-Institute for Driver Assistance Systems and Connected Mobility, Kempten University of Applied Sciences, Junkerstraße 1A, 87734 Benningen, Germany.

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Summary
This summary is machine-generated.

This study introduces a novel artificial neural network method to estimate object velocity and direction using the motion distortion effect in scanning Light Detection and Ranging (LiDAR) sensors. This approach enables accurate velocity estimation from a single point cloud, enhancing automated vehicle perception.

Keywords:
LiDAR sensoradvanced driver assistance systemsdeep learninghighly automated drivingmotion distortion effectpoint cloudvelocity estimation

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Modern automated vehicles rely on Light Detection and Ranging (LiDAR) sensors for 3D data acquisition.
  • Current LiDAR applications primarily focus on object detection using point clouds.
  • Estimating object velocity typically requires object tracking or sensor data fusion.

Purpose of the Study:

  • To develop a method for estimating object velocity and direction using the motion distortion effect inherent in scanning LiDAR sensors.
  • To achieve velocity estimation from a single LiDAR point cloud without relying on sensor data fusion.
  • To leverage artificial neural networks for analyzing motion distortion patterns.

Main Methods:

  • Development of an artificial neural network (ANN) model.
  • Training and evaluation of the ANN using a synthetic dataset exhibiting motion distortion.
  • Utilizing the motion distortion effect as the primary data source for velocity estimation.

Main Results:

  • The proposed method accurately estimates object velocity and direction from a single point cloud.
  • Achieved a root mean squared error (RMSE) of 0.1187 m/s for axis-wise velocity estimation.
  • Reported an RMSE of 0.0815 m/s for resultant velocity estimation, with narrow confidence intervals.

Conclusions:

  • The method provides reliable 4D-LiDAR data (including velocity) from a single sensor.
  • Enables velocity estimation for objects moving independently of the sensor.
  • Enhances motion prediction and object tracking capabilities in autonomous systems.