Name
Abstract | ||
|---|---|---|
In this paper, we consider the development of a rotorcraft micro aerial vehicle (MAV) system capable of vision-based state estimation in complex environments. We pursue a systems solution for the hardware and software to enable autonomous flight with a small rotorcraft in complex indoor and outdoor environments using only onboard vision and inertial sensors. As rotorcrafts frequently operate in hover or nearhover conditions, we propose a vision-based state estimation approach that does not drift when the vehicle remains stationary. The vision-based estimation approach combines the advantages of monocular vision (range, faster processing) with that of stereo vision (availability of scale and depth information), while overcoming several disadvantages of both. Specifically, our system relies on fisheye camera images at 25 Hz and imagery from a second camera at a much lower frequency for metric scale initialization and failure recovery. This estimate is fused with IMU information to yield state estimates at 100 Hz for feedback control. We show indoor experimental results with performance benchmarking and illustrate the autonomous operation of the system in challenging indoor and outdoor environments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630808 | ICRA |
Keywords | Field | DocType |
rotorcraft microaerial vehicle system,inertial sensors,vision-based state estimation,autonomous rotorcraft mavs,state estimation,complex environments,onboard vision,helicopters,autonomous aerial vehicles,feedback control,frequency 25 hz,microrobots,frequency 100 hz,autonomous flight,fisheye camera images,robot vision,vectors,robots,sensors | Monocular vision,Metric system,Computer vision,Stereopsis,Vision based,Control engineering,Software,Inertial measurement unit,Artificial intelligence,Engineering,Initialization,Benchmarking | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-4673-5641-1 | 32 | 1.44 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaojie Shen | 1 | 720 | 54.75 |
| Yash Mulgaonkar | 2 | 238 | 13.90 |
| Nathan Michael | 3 | 1892 | 131.29 |
| Vijay Kumar | 4 | 7086 | 693.29 |