Detection and Restoration of Saturated Laser Waveforms Without Prior Knowledge

The avalanche photodiode equipped in LiDAR heavily relies on the reliability of the gain amplifier to consistently output a sufficient voltage signal, enabling the weak return signal to be captured. However, in certain situations, an increase in voltage will lead to saturation, which distorts the full waveform and results in inaccurate coordinate and intensity readings, causing issues in various applications. To minimize data voids and eliminate artifacts related to the analysis of clipped waveforms, this study focuses on developing an automated framework [Detection and Restoration of Saturated Waveforms without Prior Information (DRSW)] for detecting and recovering saturated waveforms associated with high reflectivity or near-field targets without any prior knowledge. The framework designs four feature descriptors based on topographic, intensity, and waveform characteristics to differentiate between saturated and unsaturated signals, enabling automatic segmentation into two categories. To address the challenges posed by signal saturation-induced peak flattening and broadening, a combined halves-Gaussian model (CHGM) is crucially presented to describe the rising and falling edges of saturated waveform with different halves of normal Gaussian functions (halves-Gaussian functions). In the CHGM, background noise is segmented rather than as a whole to improve modeling accuracy. The effectiveness of CHGM in reconstructing saturated waveforms is evaluated through rigorous comparative experiments with cubic spline, Kriging, and Gaussian mixture model (GMM) methods. The results indicate that the maximum correction error of the CHGM is limited to within 7 DN, and the peak position remains unaltered without any shift.