DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather

TL;DR

The Winter Adverse Driving dataSet (WADS) collected in the snow belt region of Michigan's Upper Peninsula is introduced and the Dynamic Statistical Outlier Removal (DSOR) filter is presented, a statistical PCL-based filter capable or removing snow with a higher recall than the state of the art snow de-noising filter while being 28\% faster.

Abstract

For autonomous vehicles to viably replace human drivers they must contend with inclement weather. Falling rain and snow introduce noise in LiDAR returns resulting in both false positive and false negative object detections. In this article we introduce the Winter Adverse Driving dataSet (WADS) collected in the snow belt region of Michigan's Upper Peninsula. WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather; weather that would cause an experienced driver to alter their driving behavior. We have labelled and will make available over 7 GB or 3.6 billion labelled LiDAR points out of over 26 TB of total LiDAR and camera data collected. We also present the Dynamic Statistical Outlier Removal (DSOR) filter, a statistical PCL-based filter capable or removing snow with a higher recall than the state of the art snow de-noising filter while being 28\% faster. Further, the DSOR filter is shown to have a lower time complexity compared to the state of the art resulting in an improved scalability. Our labeled dataset and DSOR filter will be made available at https://bitbucket.org/autonomymtu/dsor_filter

Figures (7)

• Figure 1: Part of our Winter Adverse Driving dataSet (WADS) showing moderate snowfall 0.6 in/hr (1.5 cm/hr). (top) Clutter from snow particles obscures LiDAR point clouds and reduces visibility. Here, two oncoming vehicles are concealed by the snow. (bottom) Our DSOR filter is faster at de-noising snow clutter than the state of the art and enables object detection in moderate and severe snow.
• Figure 2: A labeled sequence from the WADS dataset. Every point has a unique label and represents one of 22 classes. Active falling snow (beige) and accumulated snow (off-white) are unique to our dataset.
• Figure 3: Distribution of classes in the WADS dataset. Scenes from sub-urban driving including vehicles, roads, and man-made structures are included. Two novel classes: falling-snow and accumulated-snow are introduced to improve AV perception in adverse winter weather.
• Figure 4: Qualitative comparison of the SOR filter, state of the art DROR filter and our DSOR filter (left to right). The original point cloud shows snow clutter (orange points) that degrades LiDAR perception. The DSOR filter removes more snow compared to both the SOR and DROR filters and preserves most of the environmental features.
• Figure 5: Percentage of filtered points as a function of range (averaged over 100 point clouds). The DSOR filter outperforms both the SOR and DROR filters in ranges $<$ 20m where most of the snow is concentrated.