An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle

Yang, Shaowu; Scherer, Sebastian; Zell, Andreas

doi:10.1007/s10846-012-9749-7

Cited by 150 publications

(103 citation statements)

References 21 publications

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“…To eliminate drift, various monocular SLAM methods have been investigated on quadrocopters, both with offboard [5] and on-board processing [6,20,21]. A particular challenge for monocular SLAM is that the scale of the map needs to be estimated from additional metric sensors such as an air pressure sensor as it cannot be recovered from vision alone.…”

Section: Related Workmentioning

confidence: 99%

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Engel

Sturm

Cremers

2014

Robotics and Autonomous Systems

184

118

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We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter's flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.

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Section: Related Workmentioning

confidence: 99%

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Engel

Sturm

Cremers

2014

Robotics and Autonomous Systems

184

118

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“…In addition, a more powerful onboard PC is required for the real-time control in [34]. The same problem arises in [35], where the "H" shape landing pattern is detected in realtime, but with a powerful onboard PC. Our solution provides sufficient sensitivity of detection and precision for the MAVgroup stabilization and satisfies computational requirements of onboard embedded systems carried by lightweight MAVs.…”

Section: B Systems Of Relative Localization Of Autonomous Robotsmentioning

confidence: 99%

“…Then, we calculate the eigenvalues λ 0 , λ 1 , λ 2 and eigenvectors q 0 , q 1 , q 2 of the conic Q and use them to obtain the position of the pattern in space by the equations presented in [37]:…”

Section: System For Relative Localizationmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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“…The computer then remotely controls the MAV. An example for an approach with on-board processing can be found in [24], where the authors track the location of a landing pad. Because the geometry of this pad is known, it is possible to infer the MAV's relative pose from the observed perspective projection.…”

Section: Related Workmentioning

confidence: 99%

On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

Schauwecker

Zell

2013

J Intell Robot Syst

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We present a quadrotor Micro Aerial Vehicle (MAV) equipped with four cameras, which are arranged in two stereo configurations. The MAV is able to perform stereo matching for each camera pair on-board and in real-time, using an efficient sparse stereo method. In case of the camera pair that is facing forward, the stereo matching results are used for a reduced stereo SLAM system. The other camera pair, which is facing downwards, is used for ground plane detection and tracking. Hence, we are able to obtain a full 6DoF pose estimate from each camera pair, which we fuse with inertial measurements in an extended Kalman filter. Special care is taken to compensate various drift errors. In an evaluation we show that using two instead of one camera pair significantly increases the pose estimation accuracy and robustness.

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An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle

Cited by 150 publications

References 21 publications

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

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