Learn to Model and Filter Point Cloud Noise for a Near-Infrared ToF LiDAR in Adverse Weather
Tao Yang, Qiyan Yu, You Li, Zhi Yan
Abstract
Light detection and ranging (LiDAR) limitations in adverse weather (e.g., rain, fog, and snow) prevent adopting high-level autonomous vehicles in all weather conditions. Furthermore, collecting and annotating these sparse point clouds in adverse weather is often cumbersome, inefficient, and expensive. In this article, we propose a data-driven approach to statistically model the performance of a popular near-infrared (NIR) time-of-flight (ToF) LiDAR in fog, with noisy point clouds collected in a well-controlled artificial fog chamber. Given manually defined visibility describing the levels of fog, our models can directly forecast a probability distribution of a laser’s noisy range measurement. Moreover, the real road data collected in clear weather is utilized to produce auto-labeled noisy point clouds using a LiDAR performance simulator, which is then used to train a semantic segmentation network to recognize point cloud noise in the real world in adverse weather. Qualitative and quantitative experimental results verify the applicability of our LiDAR performance models in fog and show how our Sim2Real strategy of the denoising algorithm can be applied to noisy point clouds under various weather conditions. The developed robot operating system (ROS) packages are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cavayangtao/lanoise_pp</uri> .