Name
Abstract | ||
|---|---|---|
Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and reliability issue remain challenging due to the complexity and ambiguity of real world images and mechanical constraints of real robots. We present an intuitive approach to show that by accurately measuring the capability of a local controller, large-scale visual topological navigation can be achieved while being scalable and robust. Our approach achieves state-of-the-art results in trajectory following and planning in large-scale environments. It also generalizes well to real robots and new environments without retraining or finetuning. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICRA40945.2020.9196644 | 2020 IEEE International Conference on Robotics and Automation (ICRA) |
Keywords | DocType | Volume |
deep learning,robot perception,reliability issue,world images,mechanical constraints,local controller,large-scale visual topological navigation,large-scale environments,local control,large-scale topological navigation | Conference | 2020 |
Issue | ISSN | ISBN |
1 | 1050-4729 | 978-1-7281-7396-2 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyun Meng | 1 | 13 | 3.71 |
| Nathan D. Ratliff | 2 | 834 | 50.98 |
| Yu Xiang | 3 | 629 | 23.04 |
| Dieter Fox | 4 | 12306 | 1289.74 |