Modeling the Use of LiDAR through Adverse Weather

Abstract

Due to the outstanding characteristics of LiDAR imaging systems, they seem essential for the consolidation of novel applications related to computer vision, in fields such as autonomous vehicles, outdoor recognition, and surveillance. However, the final technology implementation still has some uncertainties and needs in-depth work for its use in these real-world applications. Under the presence of adverse weather conditions, for example in fog, LiDAR performance is heavily influenced and the quality of the detection becomes severely degraded. The range is reduced due to the dispersion of the media and the sensor could be saturated due to backscattering or deliver a very limited range. Light propagation modeling through turbid media is used as a tool to understand and study these phenomena. Mie Theory allows the characterization of the optical media and light-particle interactions. Monte-Carlo methods are used to solve the radiative transfer problem related to these situations. When working with those models, the results obtained are in accordance with the ones shown in experimental tests, and it is possible to predict the necessities and problems of the designed systems.