Name
Abstract | ||
|---|---|---|
This paper addresses the problem of safety in mining applications. It presents new metrics that can be used to determine dangerous situations during mine operation in real time. It also presents a fast and robust algorithm for extracting significant places from information logged by a state-of-the-art collision avoidance system. Determining significant places provides valuable context information in a variety of applications such as map building, vehicle tracking and user assistance. In our case, we are interested in obtaining context information as a preliminary step towards improving mining safety. The algorithm presented here is validated with experimental data obtained from a fleet of haulage vehicles operating in various open pit mines. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ROBOT.2009.5152475 | ICRA |
Keywords | Field | DocType |
map building,haulage vehicle,dangerous situation,context information,robust algorithm,experimental data,mining safety,mining gps data,mining application,valuable context information,significant place,global positioning system,feedback,mining,vehicle tracking,data mining,kernel,geographic information systems,data handling,real time | Geographic information system,Data mining,User assistance,Transport engineering,Control engineering,Open-pit mining,Global Positioning System,Collision avoidance system,Engineering,Vehicle tracking system,Group method of data handling,Haulage | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1050-4729 |
Citations | PageRank | References |
15 | 0.91 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Agamennoni | 1 | 194 | 16.42 |
| Juan I. Nieto | 2 | 939 | 88.52 |
| Eduardo Nebot | 3 | 126 | 11.54 |