In this paper, an automatic calibration algorithm 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 laser is static,
the amplitude and phase delay of internal stray light are estimated using the
Gaussian mixture model (GMM) and particle swarm optimization (PSO).
Specifically, the pixel positions in a raw signal amplitude map of calibration
checkboard are segmented by GMM with two clusters considering the dark and
bright image pattern. The loss function is then defined as L1-norm of
difference between mean depths of two amplitude-segmented clusters. To avoid
overfitting at a specific distance in PSO process, the calibration check board
is actually measured at multiple distances and the average of corresponding L1
loss functions is chosen as the actual loss. Such loss is minimized by PSO to
find the two optimal target parameters: the amplitude and phase delay of
internal stray light. According to the validation of the proposed algorithm,
the original loss is reduced from tens of centimeters to 3.2 mm when the
measured distances of the calibration checkboard are between 1 m and 4 m. This
accurate calibration performance is also maintained in geometrically complex
measured scene. The proposed internal stray light calibration algorithm in this
paper can be used for any type of AMCW coaxial scanning LiDAR regardless of its
optical characteristics