Name
Abstract | ||
|---|---|---|
We propose a new generative model-based predictive display for robotic teleoperation over high-latency communication links. Our method is capable of rendering photorealistic images of the scene to the human operator in real time from RGB-D images acquired by the remote robot. A preliminary exploration stage is used to build a coarse 3D map of the remote environment and to train a generative model, both of which are then used to generate photo-realistic images for the human operator based on the commanded pose of the robot. Data captured by the remote robot is used to dynamically update the 3D map, enabling teleoperation in the presence of new and relocated objects. Various experiments validate our proposed method's performance and benefits over alternative methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561787 | 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 | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bowen Xie | 1 | 0 | 0.68 |
| Mingjie Han | 2 | 0 | 0.68 |
| Jin Jun | 3 | 2 | 4.82 |
| Martin Barczyk | 4 | 30 | 3.69 |
| Martin Jagersand | 5 | 100 | 10.96 |