Name
Abstract | ||
|---|---|---|
In this paper we introduce a method for classifying terrains and for predicting friction coefficient on terrains by applying visual information. Coefficient of friction on a terrain is very important for autonomous mobile robots in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. To predict friction coefficient from given image, we divide an image into homogeneous regions which have same material composition. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. Obtained information about friction coefficient before such terrain is entered can be very useful for path planning and avoiding slippery areas. We form a group of each terrain type. So, when new terrain is entered into a system, the data of new terrain are classified into each group. To improve accuracy of the result of classifying terrains, images are compensated by using contrast enhancement techniques. By grouping each terrain to use the same regression coefficients, we can reduce the amount of processing time for terrain recognition. The proposed method will be verified by real outdoor environment with real vehicles. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ROBIO.2009.5420568 | ROBIO |
Keywords | Field | DocType |
friction,noncontacting approach,road surface,visual information,terrain classification,regression coefficient,contrast enhancement,friction coefficient,autonomous mobile robot,homogeneous regions,mobile robots,noncontact terrain classification,regression coefficients,classifying terrains,terrain type,material characteristics,image classification,visual image,path planning,new terrain,non-contact terrain classification,terrain recognition,asphalt,classification algorithms,image segmentation | Motion planning,Computer vision,Terrain,Image segmentation,Control engineering,Road surface,Artificial intelligence,Engineering,Contextual image classification,Statistical classification,Mobile robot,Traverse | Conference |
ISBN | Citations | PageRank |
978-1-4244-4775-6 | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ja-young Kim | 1 | 56 | 10.72 |
| Doogyu Kim | 2 | 0 | 0.68 |
| Jong-hwa Lee | 3 | 40 | 7.55 |
| Jihong Lee | 4 | 79 | 19.85 |
| Hanbyul Joo | 5 | 101 | 5.17 |
| In So Kweon | 6 | 2795 | 207.62 |