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A Method of Calibration for the Distortion of LiDAR Integrating IMU and Odometer.

Qiuxuan WuQinyuan MengYangyang TianZhongrong ZhouCenfeng LuoWandeng MaoPingliang ZengBotao ZhangYanbin Luo
Published in: Sensors (Basel, Switzerland) (2022)
To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy.
Keyphrases
  • machine learning
  • electronic health record
  • big data
  • deep learning
  • computed tomography
  • neural network
  • magnetic resonance imaging
  • artificial intelligence
  • healthcare
  • social media
  • blood flow