Towards a navigation system for autonomous indoor flying

By Slawomir Grzonka, Giorgio Grisetti, Wolfram Burgard. IEEE International Conference on Robotics and Automation, 2009

302 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/69851cfa7b64ddc67ae71b5aad583a81e3ef0175

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In Aerospace Engineering

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems (2012, methodology): “As in Grzonka et al. (2009), some of the LIDAR beams were deflected downward to estimate and control height above the ground plane.”
State estimation for aggressive flight in GPS-denied environments using onboard sensing (2012, background): “Prior work in our group [2], as well as others [18], [6] leveraged a 2D laser rangefinder to perform SLAM from a quadrotor in GPS-denied environments.”
A Fully Autonomous Indoor Quadrotor (2012, methodology): “This paper extends ou r previous work [17] by introducing improved algorithms for”
Differential flatness based control of a rotorcraft for aggressive maneuvers (2011, background): “INTRODUCTION Simple rotorcraft are becoming increasingly utilized for aerial robotics research [15], [8], [16], [1], [7], [3].”
Natural language command of an autonomous micro-air vehicle (2010, background): “Here, we leverage that system to localize and control the vehicle in a previously explored, known environment [3], [4].”
Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model (2014, background): “In many publications, like [3], [11], [18] for example, a subtle mistake is made when relating gravity to the accelerometer measurements on a quadrotor.”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “Initial developments in the area focused on systems capable of navigating indoor environments with algorithms leveraging laser and IMU information to generate a map and localize within the map (He et al., 2008; Grzonka et al., 2009; Bachrach et al., 2011).”
Autonomous multi-floor indoor navigation with a computationally constrained MAV (2011, methodology): “Relevant to this paper is the work of Bachrach et al. [1] [2], Grzonka et al. [3], and Blösch et al. [4] with results toward online autonomous navigation and exploration with an aerial vehicle.”
The true role of accelerometer feedback in quadrotor control (2010, background): “Representative designs are: ultrasonic rangers [3]; (simple) GPS module when outdoors and infrared rangers when indoors [4]; carrier phase differential GPS [5]; laser rangefinder [6]; vision system [7], [8], [9]; laser rangefinder and vision system [10].”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, background): “Laser range ﬁnders are also used in [8], [12] which focus on pose estimation and SLAM without autonomous ﬂight behaviors.”
A flexible and scalable SLAM system with full 3D motion estimation (2011, methodology): “Multiple indoor navigation systems based on laser scan matching have been presented for the use on Quadrotor UAVs [6], [7], [8].”
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].”
Autonomous flight in unstructured and unknown indoor environments (2009, methodology): “Recently, [14, 15] designed helicopter configurations that were similar to the one presented in [13].”
A bearing-only 2D/3D-homing method under a visual servoing framework (2010, methodology): “RELATED WORK The latest result in navigation for autonomous flying robot is given by Grzonka et al. [13].”
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera (2011, background): “Finally, there has been considerable work in using laser range finders for MAV navigation and control [13, 3, 11, 34] with the limitations discussed earlier in this paper.”
Camera-based navigation of a low-cost quadrocopter (2012, unknown): “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.”
Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue (2012, background): “Therefore, the authors in [4] and [5] chose to send laser scanner data to a ground station for processing.”