Name
Abstract | ||
|---|---|---|
This paper describes a novel framework for autonomous exploration in large and complex environments. We show that the framework is efficient as a result of its hierarchical structure, where at one level it maintains a sparse representation of the environment and at another level, a dense representation is used within a local planning horizon around the robot. The exploration path is computed at the two levels, coarsely at the global scale and finely around the robot. Such a framework produces detailed paths in the vicinity of the robot, while trades off data resolution far away from the robot for computational efficiency. In experiments, we evaluate our method with a real robot exploring large and complex indoor and outdoor environments. Results show that our method is twice as efficient in covering spaces while using less than one-fifth of processing in comparison to state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561916 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cao Chao | 1 | 17 | 4.57 |
| Hongbiao Zhu | 2 | 0 | 1.69 |
| Howie Choset | 3 | 2826 | 257.12 |
| Ji Zhang | 4 | 33 | 5.56 |