Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments

By Markus Achtelik, Michael Achtelik, Stephan Weiss, Roland Yves Siegwart. IEEE International Conference on Robotics and Automation, 2011

344 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e2d809846019b4ff5efd699d9600489a4f7ba5bf

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, background): “Markerless computer vision control based on a single downward-looking camera with wide-angle lens has been demonstrated for landing, takeoff, and hovering and smallscale mapping in [1], [5].”
Control of a Quadrotor With Reinforcement Learning (2017, methodology): “The ascending Technology framework [20] is exploited to interface with the vehicle as shown in Fig.”
Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue (2012, methodology): “This differs from other approaches, where either laser odometry [6] or monocular visual odometry [7] is used for pose estimation.”
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.”
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments (2012, result): “We do not focus on the control itself in this paper but refer to our previous work [6].”
Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint (2016, background): “may vary and affect not only the channel conditions [180] but also the device hardware [181].”
Large-scale direct SLAM for omnidirectional cameras (2015, background): “A lot of research has been focused around these topics throughout the last decade, with a particular focus on real-time systems – which can be used for autonomous control for example of UAVs [1], [2].”
Learning monocular reactive UAV control in cluttered natural environments (2012, methodology): “We successfully avoided more than 680 trees during flights over a distance of more than 3 km.”
PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms (2015, background): “the average robotic application will also provide a companion computer, commonly running ROS on Linux [18][19][3].”
Pushbroom stereo for high-speed navigation in cluttered environments (2014, background): “When integrating inertial measurement sensors and a barometric altimeter, stable flights in indoor and outdoor environments are possible [1].”
Camera-based navigation of a low-cost quadrocopter (2012, neutral): “For solving the SLAM problem on MAVs, different types of sensors such laser range scanners [5], monocular cameras [6, 7], stereo cameras [8] and RGB-D sensors [9] have been explored in the past.”
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, background): “The proposed flying system carries up to 6× the computation power of comparable systems of the same size, e.g. Achtelik et al. (2011).”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “The design of quad-rotors has been analyzed and presented by many authors, for instance: Pounds et al. (2002); Bouabdallah (2007); Gurdan et al. (2007); Achtelik et al. (2011a).”