Autonomous multi-floor indoor navigation with a computationally constrained MAV

By Shaojie Shen, Nathan Michael, Vijay R. Kumar. 2011

417 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, methodology): “Although methods based on geometric reconstruction and mapping have proven effective in a range of navigation and collision avoidance domains [1], [2], [3], they impose considerable computational overhead [4] and”
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.”
Collaborative mapping of an earthquake‐damaged building via ground and aerial robots (2012, methodology): “…but greater than 8 cm in either the x or y directions, the vehicle will enter a recovery phase in which it will attempt to regulate 1 cm above the desired landing location and concurrently estimate the changes in the dynamic model resulting from ground effects (as discussed in Shen et al. (2011)).”
Stereo vision based indoor/outdoor navigation for flying robots (2013, methodology): “Shen et al. [6] also utilize a laser scanner for multi-ﬂoor navigation, but perform all computations including SLAM with loop closure on-board the MAV.”
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, methodology): “Indeed, in our previous work [1], we developed a state estimation method based on laser and IMU sensors to enable a MAV to fly autonomously in a structured indoor environment.”
A robust and modular multi-sensor fusion approach applied to MAV navigation (2013, background): “Common fusion approaches are based on indirect formulations of Extended (EKF) [13], or Unscented (UKF) Kalman Filters [14].”
Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model (2014, methodology): “In [2], a quadrotor system is presented that also uses a scanning laser range finder.”
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, background): “Recent works successfully demonstrated the use of small LIDAR sensors on such small scale MAV’s for mapping and autonomous flight [13,27,8,30,2,11].”
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments (2012, background): “However, many implementations still rely on artificial features [1] or heavier and costly sensors like laser scanners [2].”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, background): “Many researchers demonstrated some of these abilities by using laser range ﬁnders [2], [8], [22], artiﬁcial marker aided vision [9], [19] and/or a Vicon motion capture system [15].”
Opportunities and challenges with autonomous micro aerial vehicles (2012, methodology): “These methods can also be used to determine the offsets in inertial measurement unit (IMU) readings and for online calibration (Shen et al., 2011b).”
Towards a swarm of agile micro quadrotors (2012, background): “2kg gross weight, 0.75m diameter, and 5,400rpm nominal rotor speed at hover), consumes approximately 400W of power (Shen et al. 2011).”
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.”
Progress on ‘pico’ air vehicles (2012, result): “This is in contrast to ‘higher-level’ control problems of autonomous navigation (Shen et al., 2011) and coordination of multiple unmanned air vehicles (Stirling and Floreano, 2010).”