Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 1, Pages 749-768Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2993454
Keywords
Feature extraction; Data mining; Three-dimensional displays; Roads; Laser radar; Shape; Clustering algorithms; 3-D point cloud; extraction; mobile Light Detection and Ranging (LiDAR); pole-like objects
Categories
Funding
- National Key Research and Development Program of China [2017YFB1302400]
- National Natural Science Foundation of China [41571436]
- Hubei Province Science and Technology Support Program, China [2015BAA027]
Ask authors/readers for more resources
The proposed algorithm in this article extracts pole-like objects from mobile LiDAR data through plane filtering. It consists of two parts: the first part fits initial planes from off-ground points using an octree-based split scheme, while the second part extracts pole-like objects based on local geometric features and isolation detection. Experimental results show that the approach can robustly and efficiently extract pole-like objects in various scenes.
Pole-like objects provide important street infra- structure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66, 88.81, and 79.03, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available