Paper Info
Title
Ground object recognition and segmentation from aerial image-based 3D point cloud.
Abstract
Several attempts have been made to grasp three-dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low-level point information (coordinates and colors) and high-level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo-referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance-based clustering and color-based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.
Year
DOI
Venue
2019
10.1111/coin.12232
COMPUTATIONAL INTELLIGENCE
Keywords
Field
DocType
drone,outdoor recognition,point cloud,segmentation,3D objects
Pattern recognition,Segmentation,Computer science,Aerial image,Artificial intelligence,Drone,Point cloud,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
35.0
SP3.0
0824-7935
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Katsuya Ogura100.68
Yuma Yamada211.36
Shugo Kajita343.16
Hirozumi Yamaguchi437160.93
Higashino, T.51915.19
Mineo Takai6893127.45