AbstractThree dimension (3D) point cloud data in fog-filled environments were measured using light detection and ranging (LIDAR). Disaster response robots cannot easily navigate through such environments because this data contain false data and distance errors caused by fog. We propose a method for recognizing and removing fog based on 3D point cloud features and a distance correction method for reducing measurement errors. Laser intensity and geometrical features are used to recognize false data. However, these features are not sufficient to measure a 3D point cloud in fog-filled environments with 6 and 2 m visibility, as misjudgments occur. To reduce misjudgment, laser beam penetration features were added. Support vector machine (SVM) and K-nearest neighbor (KNN) are used to classify point cloud data into ‘fog’ and ‘objects.’ We evaluated our method in heavy fog (6 and 2 m visibility). SVM has a better F-measure than KNN; it is higher than 90% in heavy fog (6 and 2 m visibility). The distance error corr...
['Abu Ubaidah Shamsudin', 'Kazunori Ohno', 'Thomas Westfechtel', 'Suzuki Takahiro', 'Yoshito Okada', 'Satoshi Tadokoro']
Fog removal using laser beam penetration, laser intensity, and geometrical features for 3D measurements in fog-filled room