LaNoising: A Data-driven Approach for 903nm ToF LiDAR Performance Modeling under Fog

As a critical sensor for high-level autonomous vehicles, LiDAR's limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. In this paper, we model the performance of a popular 903nm ToF LiDAR under various fog conditions based on a LiDAR dataset collected in a well-controlled artificial fog chamber. Specifically, a two-stage data-driven method, called LaNoising (la for laser), is proposed for generating LiDAR measurements under fog conditions. In the first stage, the Gaussian Process Regression (GPR) model is established to predict whether a laser can successfully output a true detection range or not, given certain fog visibility values. If not, then in the second stage, the Mixture Density Network (MDN) is used to provide a probability prediction of the noisy measurement range. The performance of the proposed method has been quantitatively and qualitatively evaluated. Experimental results show that our approach can provide a promising description of 903nm ToF LiDAR performance under fog.