Name
Abstract | ||
|---|---|---|
Scan registration has a critical role in mapping and localization for Autonomous Ground Vehicle (AGV). This paper addresses the problem of alignment with only exploiting the common static objects instead of the whole point clouds or entire patches on campus environments. Particularly, we wish to use instances of classes including trees, street lamps and poles amongst the whole scene. The distinct advantage lies in it can cut the number of pairwise points down to a quite low level. A binary trained Support Vector Machine (SVM) is used to classify the segmented patches as foreground or background according to the extracted features at object level. The Iterative Closest Point (ICP) approach is adopted only in the foreground objects given an initial guesses with GPS. Experiments show our method is real-time and robust even when the the signal of GPS suddenly shifts or invalid in the sheltered environment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45646-0_38 | Communications in Computer and Information Science |
Keywords | Field | DocType |
scan registration,object level,binary classification,autonomous ground vehicle | Autonomous ground vehicle,Computer vision,Pairwise comparison,Binary classification,Computer science,Support vector machine,Artificial intelligence,Global Positioning System,Point cloud,Binary number,Iterative closest point | Conference |
Volume | ISSN | Citations |
483 | 1865-0929 | 2 |
PageRank | References | Authors |
0.40 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chongyang Wei | 1 | 3 | 1.42 |
| Shuangyin Shang | 2 | 2 | 0.40 |
| Tao Wu | 3 | 58 | 11.53 |
| Hao Fu | 4 | 10 | 2.51 |