Name
Title | ||
|---|---|---|
Turning Mobile Laser Scanning Points Into 2d/3d On-Road Object Models: Current Status |
Abstract | ||
|---|---|---|
Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Mobile laser scanning (MLS), 3D point clouds, object extraction, road |
Field | DocType | ISSN |
Computer vision,Computer science,Mobile laser scanning,Remote sensing,Automation,Feature extraction,Lidar,Artificial intelligence,Road surveying,Mobile telephony | Conference | 2153-6996 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongliang Zhang | 1 | 1 | 2.04 |
| Ming Cheng | 2 | 54 | 13.93 |
| Xinqu Chen | 3 | 1 | 0.69 |
| Menglan Zhou | 4 | 1 | 0.35 |
| Yifei Chen | 5 | 1 | 0.35 |
| Jonathan Li | 6 | 798 | 119.18 |
| Hongshan Nie | 7 | 2 | 1.04 |