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Abstract Georeferencing means to transform the point cloud data collected by the terrestrial laser scanner (TLS) into a global coordinate system for three-dimensional (3D) scene reconstruction. However, the current georeferencing methods are mainly applicable for the TLS operating in the static mode; when the TLS is moving (i.e., in the kinematic working mode) during the 3D scene reconstruction, severe georeferencing error will be generated due to the kinematic measurement noise and geometric falsification offsets. To bridge this research gap, a new method based on the kinemics state constraints converted measuring Kalman Filter (KSC-CMKF) and the Levenberg Marquardt Wavelet neural network (LM-WNN) is proposed to correct the TLS georeferencing error in the kinematic operating mode (i.e., TLS kinematic georeferencing error). This new method can automatically correct the georeferencing error and simultaneously reduce the TLS systematic bias to save time and cost for the kinematic geor

Kalman-filter
Levenberg-marquardt-wavelet
Error-correction
Eoreferencing
Inematic-modeling
Terrestrial-laser-scanner
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