Autonomous MAV flight in indoor environments using single image perspective cues
By Cooper Bills, Joyce Chen, Ashutosh Saxena. 2011
240 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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
- Sergey Levine
- Vijay R. Kumar
- Nathan Michael
- Fereshteh Sadeghi
- J. Bagnell
- Pieter Abbeel
- Marc Pollefeys
- Shaojie Shen
In Computer Vision and Pattern Recognition
- Sergey Levine
- Jitendra Malik
- Luc Van Gool
- Abhinav Kumar Gupta
- Andrew Zisserman
- Trevor Darrell
- Dacheng Tao
- Bernt Schiele
Citations in the corpus
- (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “In contrast to the work of [5], our method directly learns to predict the probability of collision, given an image and a candidate action, without attempting to explicitly detect obstacles.”
- Learning monocular reactive UAV control in cluttered natural environments (2012, background): “Hadsell et al. [23] showed in the LAGR project how to recognize and avoid obstacles within complex outdoor environments using vision and deep hierarchical networks.”
- Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, methodology): “Bills et al. [4] implemented an approach for higher level navigation using a Parrot AR.Drone.”
- Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, background): “At the other end of the spectrum, there are many successful attempts at integrating vision for autonomous flight which do not rely on estimates of metric state estimation [11, 12].”
- Camera-based navigation of a low-cost quadrocopter (2012, unknown): “Two videos demonstrating the robustness of our approach, its ability to eliminate drift effectively and to estimate the absolute scale of the map are available online: http://youtu.be/tZxlDly7lno http://youtu.be/eznMokFQmpc”
- Deep Neural Network for Real-Time Autonomous Indoor Navigation (2015, background): “Bills et al. [15] used a monocular camera and found vanishing points.”
- Real-time 3D scene layout from a single image using Convolutional Neural Networks (2016, unknown): “[11], detecting wall-floor intersection landmarks [12] or building a simplified 3D model [2], as mentioned above.”
- PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, methodology): “An approach for higher level navigation implemented on a Parrot AR.Drone was described by Bills et al. (2011).”