• Corpus ID: 237513664

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

  title={DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather},
  author={Akhil Kurup and Jeremy P. Bos},
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… 

Figures and Tables from this paper

A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter
Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive
3D ToF LiDAR in Mobile Robotics: A Review
This article systematically reviews and analyzes the use of 3D ToF LiDARs in research and industrial applications, which includes object detection, robot localization, long-term autonomy,LiDAR data processing under adverse weather conditions, and sensor fusion.


De-noising of Lidar Point Clouds Corrupted by Snowfall
This paper presents a method for removing snow noise by processing point clouds using a 3D outlier detection algorithm, and shows on point clouds obtained while driving in falling snow that it can simultaneously obtain > 90% precision and recall.
Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds
A new intensity-based filter that differs from the existing distance- based filter, which limits the speed is proposed, which showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features.
Towards Characterizing the Behavior of LiDARs in Snowy Conditions
Autonomous driving vehicles must be able to handle difficult weather conditions in order to gain acceptance. For example, challenging situations such as falling snow could significantly affect the
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
Semantic Point Generation (SPG), a general approach to enhance the reliability of LiDAR detectors against domain shifts, is presented, which significantly improves both detectors across all object categories of interest and at all difficulty levels.
Autonomy at the end of the earth: an inclement weather autonomous driving data set
We have collected an extensive winter autonomous driving data set consisting of over 4TB of data collected between November 2019 and March 2020. Our base configuration features two 16 channel LiDARs,
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction
A new noise reduction method to filter LiDAR point clouds, i.e. an adaptive clustering method based on principal component analysis (PCA), which derives low computational complexity, effectively removing noises while retaining details of environmental features.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
A large dataset to propel research on laser-based semantic segmentation, which opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.
Physical model of snow precipitation interaction with a 3D lidar scanner.
The randomness and the intensity of the signal as a function of the visibility and snowflake size and density distribution are reproduced and a filtering algorithm based on the relativeintensity of the snowflakes is discussed.
nuScenes: A Multimodal Dataset for Autonomous Driving
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object