Light detection and ranging (LiDAR), is an active three-dimensional perception sensor with high scan speed, high spatial resolution and precise distance measurement, commonly applied in autonomous vehicles, surveying and mapping, military and defense, et al [1]. By emitting laser pulses to interact with surrounding objects, LiDAR captures the reflected signals, enabling the computation of distances and the creation of point clouds [2].
However, the aerosol particle clusters in fog and rain lead to energy attenuation and echo distortion, resulting in the generation of heterogeneous point clouds, impeding the target detection and environmental perception capability of LiDAR [3]. Therefore, exploring the generation mechanism of lidar noise rain or fog noise in LiDAR is important for suppressing noises and enhancing LiDAR’s imaging performance [4].
From a paper by R. Yu, et al.
Current research generally adopts two approaches to simulate LiDAR signals under various kinds of weather: mathematical modelling [5], [6] or the Monte Carlo method [7]. Mathematical modelling involves modeling the LiDAR system, target, and environment to formulate the mathematical representations of the echo signals [8], [9]. Based on the LiDAR equation, the mathematical relationship between the received and emitted power of LiDAR is established, considering the attenuation effect [10], single backscattering [11], or multiple backscattering [12].
Mathematical modeling provides an explicit description of the distorted and interfered signals in an aerosol environment, but it cannot simulate the rain or fog noise accurately and illustrate its generation mechanism clearly because it fails to capture the intricate physical motion of photons.
In contrast, the Monte Carlo method excels in modeling the photon motion, including emission, propagation, scattering, and absorption. It has been extensively employed to present statistical information about photon behavior and assess the imaging capability of LiDAR in seawater [13] or aerosol environments, such as water clouds [14], smoke [15], rain and fog [16], et al. As an effective tool, the Monte Carlo method can simulate the attenuation characteristics [17], frequency spectrum [18], and impulse response [19] of LiDAR in adverse weather, thereby providing a detailed analysis of how the aerosol environment impacts LiDAR signals [20], [21], [22].
For the complete paper on lidar noise modelling CLICK HERE.
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