Name
Abstract | ||
|---|---|---|
In this paper, we propose a LiDAR-based robot localization method in a complex oil and gas environment. Localization is achieved in six degrees of freedom (DoF) thanks to a particle filter framework. A new time-efficient likelihood function, based on a precalculated three-dimensional likelihood field, is introduced. Experiments are carried out in real environments and their digitized point clouds. Six DoF real-time localization is achieved with spatial and angular errors of less than 2.5 cm and 1 degrees, respectively, in a real environment of 350 m(3).The proposed approach focuses on real-time performance on embedded platforms. It enabled the Vikings team to win the first two ARGOS Challenge contests. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1002/rob.21735 | JOURNAL OF FIELD ROBOTICS |
Keywords | Field | DocType |
position estimation,wheeled robots,extreme environments,sensors | Computer vision,Robot localization,Likelihood function,Simulation,Six degrees of freedom,Fossil fuel,Lidar,Artificial intelligence,Engineering,Point cloud,Robotics | Journal |
Volume | Issue | ISSN |
35.0 | 2.0 | 1556-4959 |
Citations | PageRank | References |
2 | 0.41 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Merriaux, P. | 1 | 25 | 4.21 |
| Yohan Dupuis | 2 | 61 | 10.12 |
| R. Boutteau | 3 | 47 | 8.69 |
| Pascal Vasseur | 4 | 267 | 28.03 |
| Xavier Savatier | 5 | 118 | 17.42 |