Asymmetric Gaussian Echo Model for LiDAR Intensity Correction - Xinyue Ma; Haitian Jiang; Xin Jin (Remote Sensing)
In light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false intensity values generated by existing LiDAR systems working in scenarios involving highly reflective objects or short distances. To tackle the problem, an asymmetric Gaussian echo model is proposed in this paper so as to recover echo power–time curves faithfully to its optical physics. Considering the imbalance in temporal length and steepness between rising and falling edges, the echo model features a shared mean and two distinct standard deviations on both sides. The accuracy and effectiveness of the proposed model are demonstrated by correcting the power–time curve from a real LiDAR loaded with avalanche photodiode (APD) sensors and estimating the reflectivities of real targets. As when tested by targets with reflectivities from low to high placed at distances from near to far, the model achieves a maximum of 41.8-fold improvement in relative error for the same target with known reflectivity and a maximum of 36.0-fold improvement in the coefficient of variation for the same target along the whole range of 100 m. Providing accurate and stable characterization of reflectivity in different ranges, the model greatly boosts applications consisting of semantic segmentation and object recognition, such as autonomous driving and environmental monitoring.