Camera-based navigation of a low-cost quadrocopter

By Jakob J. Engel, Jürgen Sturm, Daniel Cremers. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

417 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3b911e68c0d6a3a5d3d6ee31f9fafebd0937adda

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Ocean Engineering

Citations in the corpus

Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop (2018, methodology): “The most popular off-the-shelf platforms have been the AscTec Hum-mingbird [3], [4], the AscTec Pelican [5], and the Parrot AR Drone [6].”
Pushbroom stereo for high-speed navigation in cluttered environments (2014, background): “These small aircraft, weighing under 1-2kg, struggle to carry enough sensing payload for highspeed navigation in complex 3D environments.”
Large-scale direct SLAM for omnidirectional cameras (2015, background): “We then express the straight line between these points as p L (α) = αp max + (1 − α)p min , (26) for α ∈ [0] [1].”
Robust odometry estimation for RGB-D cameras (2013, background): “Various well-working systems based on this approach have been presented over the past years [4], [9], including our own recent works [3], [15].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “Engel et al. (2012) avoided this problem, and showed autonomous indoor operation with an AR.Drone, using both its optical flow sensor, as well as PTAM.”
LSD-SLAM: Large-Scale Direct Monocular SLAM (2014, background): “Two major reasons are (1) their use in robotics, in particular to navigate unmanned aerial vehicles (UAVs) [10,8,1], and (2) augmented and virtual reality applications slowly making their way into the mass-market.”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, background): “Optical ﬂow has wide ranging applications in processing video data, from video compression [53], to video segmentation [54], motion magniﬁca-tion [55] and vision-based navigation of UAVs [56].”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, methodology): “To learn the elements of vSLAM algorithms, we provide ATAM([7]) which is a vSLAM toolkit for beginners [89].”