Vision-based state estimation for autonomous rotorcraft MAVs in complex environments
By Shaojie Shen, Yash Mulgaonkar, Nathan Michael, Vijay R. Kumar. 2013
121 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Markus Achtelik
- Roland Yves Siegwart
- Davide Scaramuzza
- Simon Lynen
- Sergey Levine
- George Nikolakopoulos
- Christoforos Kanellakis
- Stephan Weiss
In Computer Vision and Pattern Recognition
- Markus Achtelik
- Roland Yves Siegwart
- Davide Scaramuzza
- Sergey Levine
- Vladlen Koltun
- Simon Lynen
- Luc Van Gool
- Xiaogang Wang
In Ocean Engineering
- Roland Yves Siegwart
- Markus Achtelik
- Stephan Weiss
- Sergey Levine
- George Nikolakopoulos
- Christoforos Kanellakis
- Guoquan Huang
- Davide Scaramuzza
Citations in the corpus
- (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, background): “rmine its geometry, and then compute a collisionfree path for reaching the destination [29]. This approach benefits from independent developments in mapping and localization as well as motion planning [28], [20], [4]. The 3D geometry of the local environment can be deduced using SLAM with range senso”
- A robust and modular multi-sensor fusion approach applied to MAV navigation (2013, methodology): “This problem is commonly addressed by fixing the respective states in the estimation process and applying pose estimates from the visual SLAM algorithms as pseudo-absolute measurements [3], [14], [15].”
- Survey on Computer Vision for UAVs: Current Developments and Trends (2017, methodology): “Similarly in [86], a visual inertial integrated system onboard a UAV for state estimation and control for agile motion has been developed.”