Automatic Internal Stray Light Calibration of AMCW Coaxial Scanning LiDAR Using GMM and PSO
Sung-Hyun Lee, Wook-Hyeon Kwon, Yoon-Seop Lim, Yong-Hwa Park

TL;DR
This paper introduces an automatic calibration method for AMCW coaxial LiDAR that reduces depth errors caused by internal stray light using GMM and PSO, achieving millimeter-level accuracy across various scenes.
Contribution
It presents a novel calibration algorithm combining GMM and PSO to estimate and correct internal stray light effects in AMCW LiDAR, improving depth accuracy.
Findings
Reduced depth error from tens of centimeters to 3.2 mm
Effective across multiple distances and complex scenes
Applicable to various AMCW LiDAR types
Abstract
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…
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Taxonomy
TopicsAdvanced Optical Sensing Technologies · Optical measurement and interference techniques · Remote Sensing and LiDAR Applications
