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In this paper, an automatic calibration algorithm is proposed to reduce thedepth error caused by internal stray light in amplitude-modulated continuouswave (AMCW) coaxial scanning light detection and ranging (LiDAR). Assuming thatthe internal stray light generated in the process of emitting laser is static,the amplitude and phase delay of internal stray light are estimated using theGaussian mixture model (GMM) and particle swarm optimization (PSO).Specifically, the pixel positions in a raw signal amplitude map of calibrationcheckboard are segmented by GMM with two clusters considering the dark andbright image pattern. The loss function is then defined as L1-norm ofdifference between mean depths of two amplitude-segmented clusters. To avoidoverfitting at a specific distance in PSO process, the calibration check boardis actually measured at multiple distances and the average of corresponding L1loss functions is chosen as the actual loss. Such loss is minimized by PSO tofind the two optimal target parameters: the amplitude and phase delay ofinternal stray light. According to the validation of the proposed algorithm,the original loss is reduced from tens of centimeters to 3.2 mm when themeasured distances of the calibration checkboard are between 1 m and 4 m. Thisaccurate calibration performance is also maintained in geometrically complexmeasured scene. The proposed internal stray light calibration algorithm in thispaper can be used for any type of AMCW coaxial scanning LiDAR regardless of itsoptical characteristics.

Automatic Internal Stray Light Calibration of AMCW Coaxial Scanning LiDAR Using GMM and PSO