Name
Abstract | ||
|---|---|---|
On the quest of automating the navigation of challenging and promising Robotics platforms such as small Unmanned Aerial Vehicles (UAVs), the community has been increasingly active in developing perception capabilities able to run onboard such platforms in real-time. Despite that vision-based techniques have been at the heart of recent advancements, the realistic employment onboard UAVs is still in its infancy. Inspired by some of the most recent breakthroughs in online dense scene estimation and borrowing fundamental concepts from Computer Vision, in this work we propose a new pipeline for real-time, local scene reconstruction using a single camera for aerial navigation. Aiming for denser scene estimation than traditional feature-based maps with the ability to run onboard a small UAV in real-time, the proposed approach is demonstrated to achieve unprecedented performance producing rich maps of the camerau0027s workspace, timely enough to serve in obstacle avoidance and real-time interaction of a robot with its direct surroundings. Evaluation on benchmarking datasets and on challenging aerial footage captured with a UAV featuring a conventional camera, reveals dramatic speed-ups, as well as denser and more accurate local reconstructions with respect to the state of the art. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3929/ethz-a-010855647 | ICRA |
Field | DocType | Volume |
Obstacle avoidance,Iterative reconstruction,Computer vision,Workspace,Artificial intelligence,Engineering,Simultaneous localization and mapping,Robot,Benchmarking,Robotics,3D reconstruction | Conference | 2017 |
Issue | Citations | PageRank |
1 | 1 | 0.36 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Teixeira | 1 | 30 | 6.93 |
| Margarita Chli | 2 | 1283 | 53.59 |