Name
Abstract | ||
|---|---|---|
Ground segmentation is a key component for Autonomous Land Vehicle (ALV) navigation in an outdoor environment. This paper presents a novel algorithm for real-time segmenting three-dimensional scans of various terrains. An individual terrain scan is represented as a circular polar grid map that is divided into a number of segments. A one-dimensional Gaussian Process (GP) regression with a non-stationary covariance function is used to distinguish the ground points or obstacles in each segment. The proposed approach splits a large-scale ground segmentation problem into many simple GP regression problems with lower complexity, and can then get a real-time performance while yielding acceptable ground segmentation results. In order to verify the effectiveness of our approach, experiments have been carried out both on a public dataset and the data collected by our own ALV in different outdoor scenes. Our approach has been compared with two previous ground segmentation techniques. The results show that our approach can get a better trade-off between computational time and accuracy. Thus, it can lead to successive object classification and local path planning in real time. Our approach has been successfully applied to our ALV, which won the championship in the 2011 Chinese Future Challenge in the city of Ordos. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10846-013-9889-4 | Journal of Intelligent and Robotic Systems |
Keywords | Field | DocType |
Autonomous land vehicle,Ground segmentation,Gaussian process,3D point cloud,68T45,68T40,60G15 | Motion planning,Computer vision,Grid reference,Covariance function,Market segmentation,Regression,Segmentation,Terrain,Control engineering,Gaussian process,Artificial intelligence,Engineering | Journal |
Volume | Issue | ISSN |
76 | 3-4 | 0921-0296 |
Citations | PageRank | References |
27 | 1.27 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongtong Chen | 1 | 61 | 6.88 |
| Bin Dai | 2 | 27 | 1.27 |
| Ruili Wang | 3 | 446 | 50.35 |
| Daxue Liu | 4 | 116 | 10.89 |