Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments

Abstract This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment.

[1]  Nikolaos Papanikolopoulos,et al.  Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Roland Siegwart,et al.  Integrated Data Management for a Fleet of Search‐and‐rescue Robots , 2017, J. Field Robotics.

[3]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..

[4]  Abdul Nurunnabi,et al.  Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data , 2015, Pattern Recognit..

[5]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Mohammad Javad Valadan Zoej,et al.  A Novel Filtering Algorithm for Bare-Earth Extraction From Airborne Laser Scanning Data Using an Artificial Neural Network , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Yangde Zhang,et al.  Outlier detection based on the neural network for tensor estimation , 2014, Biomed. Signal Process. Control..

[8]  Roland Pail,et al.  Outlier detection algorithms and their performance in GOCE gravity field processing , 2005 .

[9]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[10]  Kiho Kwak,et al.  Probabilistic traversability map generation using 3D-LIDAR and camera , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[11]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[12]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[13]  Guido Smits,et al.  Robust outlier detection using SVM regression , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).