Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue

By Teodor Tomic, Korbinian Schmid, P. Lutz, Andreas Dömel, M. Kassecker, Elmar Mair, I. Grixa, Felix Ruess, Michael Suppa, Darius Burschka. IEEE robotics & automation magazine, 2012

822 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d2c72dd7df5d349b7daf6d10cd25de5983f4020e

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Ocean Engineering

Citations in the corpus

Survey on Computer Vision for UAVs: Current Developments and Trends (2017, background): “Similarly, in [137] an unmanned aircraft system towards autonomous navigation based on laser and stereo vision odometry has been developed.”
Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint (2016, methodology): “In [74], where a laser sensor is used for search in urban environments, the sensor data exchange frequency is set to 10 Hz.”
Robust state estimation for Micro Aerial Vehicles based on system dynamics (2015, background): “Thanks to advances in computational power, even for these otherwise computationally restricted systems, and the development of powerful estimators using one or multiple onboard sensors [3]–[5], multi rotor MAVs are more and more capable of navigating without the aid of external motion tracking syste”
Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage (2018, background): “The ability to provide visual information with low costs and high ﬂexibility makes the drones preferable equipment in tasks relating to ﬁeld coverage and monitoring, such as in wildﬁre monitoring [10], or search and rescue [11].”
Vision-based control of a quadrotor for perching on lines (2014, methodology): “Such approaches are computationally much more expensive than our approach because they have to establish a frame of reference by running a full visual SLAM framework and then compute the desired target’s pose in that frame.”
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “Some of the aforementioned developments make use of computer vision geometry to determine navigation waypoints, but only [49] used visual information to provide control feedback in real time.”
Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU (2017, background): “M ICRO Aerial Vehicles (MAVs) equipped with on-board sensors, are ideal platforms for autonomous navigation in complex and confined environments for solving tasks such as exploration [1], inspection [2], [3], mapping [4], interaction with the environment [5], [6] and, search and rescue [7].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “Further stereo vision approaches have been presented by Cover et al. (2013), and Tomic et al. (2012).”
Stereo vision based indoor/outdoor navigation for flying robots (2013, methodology): “We optimized the system for weight and integrated it into our new quadrotor platform (Figure 1), which is an advancement of our previous system [3].”