Automatic Internal Stray Light Calibration of AMCW Coaxial Scanning LiDAR Using Black and White Image Patterns - Lee, Sung-Hyun; Lim, Yoon-Seop; Kwon, Wook-Hyeon; Park, Yong-Hwa (IEEE transactions on instrumentation and measurement, 2024)
In this paper, an automatic calibration method is proposed to reduce the depth error caused by internal stray light in amplitude-modulated continuous wave (AMCW) coaxial scanning light detection and ranging (LiDAR). Assuming that the internal stray light generated in the process of emitting the laser is in the steady-state with static amplitude and phase delay but unknowns, these unknown parameters are estimated using the checkerboard images with Gaussian mixture model (GMM) and particle swarm optimization (PSO). Specifically, the pixel positions in an amplitude map of the calibration checkerboard are segmented by GMM into two clusters by considering the dark and bright image pattern. The loss function is then defined as the L1-norm of difference between the mean depth values of two amplitude-segmented clusters. To avoid overfitting at a specific distance in PSO process, the calibration checkerboard is measured at multiple distances and the average of the corresponding L1 loss functions is chosen as the final loss. This loss is minimized by PSO to find the two optimal target parameters: the amplitude and phase delay of the internal stray light. Validation of the proposed algorithm showed that when the measured distances of the calibration checkerboard are between 1 m and 4 m, there is a reduction in loss from tens of cm to 3.2 mm. This accurate calibration performance is also maintained for geometrically complicated measured scene. The proposed internal stray light calibration algorithm in this paper can be used for general type of AMCW coaxial scanning LiDAR with various optical characteristics.