Robust 3D Object Detection in Cold Weather Conditions

Abstract

LiDAR sensors are used in autonomous driving applications to accurately perceive the environment. However, they are affected by adverse weather conditions such as snow, fog, and rain. These everyday phenomena introduce unwanted noise into the measurements, severely degrading the performance of LiDAR-based perception systems. In this work, we propose a framework for improving the robustness of LiDARbased 3D object detectors against road spray. Our approach uses a state-of-the-art adverse weather detection network to filter out spray from the LiDAR point cloud, which is then used as input for the object detector. In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment. In addition to adverse weather filtering, we explore the use of radar targets to further filter false positive detections. Tests on real-world data show that our approach improves the robustness to road spray of several popular 3D object detectors.

LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model

“…A more refined approach involves resampling the entire dataset in a resolution that corresponds to the sensor's horizontal turning rate and the number of vertical channels. This method, which aligns with the depth image approach used in our research [6,34], has been validated for its effectiveness [12]. Such resampling ensures that the augmented data more accurately mirror the way LiDAR sensors capture and interpret the world, leading to more realistic and useful augmentation outcomes.…”

“…To address this limitation, recent works have explored treating adverse effects like rain, fog, and snow as noise points with specific distributions (e.g., uniform or normal) and translating entire scenes accordingly [11,12]. While systematic, these approaches struggle to capture the variability and nuances in the distribution of adverse effects in real-world environments.…”