Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision

Yang, Shaowu; Scherer, Sebastian; Schauwecker, Konstantin; Zell, Andreas

doi:10.1007/s10846-013-9906-7

Cited by 49 publications

(35 citation statements)

References 27 publications

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“…Both approaches, however, rely on accurate state information from external tracking systems. In landing control systems with onboard sensing, cameras have been popular for visual servoing, localization and mapping, and landing pad detection [6,28,29]. Mellinger and Kumar [15] efficiently generate locally optimal polynomial trajectories exploiting the differential flatness of quadrotor UAVs.…”

Section: Related Workmentioning

confidence: 99%

Risk-aware trajectory generation with application to safe quadrotor landing

Müller

Sukhatme

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In navigation tasks, mobile robots often have to deal with substantial uncertainty due to imperfect actuators and noisy sensor measurements. In this paper, we consider the problem of online trajectory generation for safe navigation in the presence of state uncertainty and the resulting deviations from the desired trajectory. Our approach combines probabilistic estimation of the a priori collision risk with efficient trajectory generation, exploiting the differential flatness of many robotic systems in an explicitly constrained polynomial trajectory representation. Through trajectory optimization, our approach allows to flexibly trade off risk against, for example, the duration of the trajectory. It is computationally efficient because each optimization step has polynomial complexity. In contrast to other approaches, our method can also optimize the trajectory duration and supports cost functions that facilitate higher-order smoothness of the trajectory. Our experiments demonstrate the performance of the approach and show that our trajectories result in substantially lower collisions probabilities compared to minimum-snap trajectories in a quadrotor landing task.

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Section: Related Workmentioning

confidence: 99%

Risk-aware trajectory generation with application to safe quadrotor landing

Müller

Sukhatme

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Quadrotor controller We use a nested PID pose controller and PD trajectory controller described in previous work [11] for autonomous navigation of our quadrotor. Those controllers are implemented based on the work in [7].…”

Section: Methodsmentioning

confidence: 99%

Robust Onboard Visual SLAM for Autonomous MAVs

Yang

Scherer

Zell

2015

Intelligent Autonomous Systems 13

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Abstract. This paper presents a visual simultaneous localization and mapping (SLAM) system consisting of a robust visual odometry and an efficient back-end with loop closure detection and pose-graph optimization. Robustness of the visual odometry is achieved by utilizing dual cameras pointing different directions with no overlap in their respective fields of view mounted on an micro aerial vehicle (MAV). The theory behind this dual-camera visual odometry can be easily extended to applications with multiple cameras. The back-end of the SLAM system maintains a keyframe-based global map, which is used for loop closure detection. An adaptive-window pose-graph optimization method is proposed to refine keyframe poses of the global map and thus correct pose drift that is inherent in the visual odometry. The position of each map point is then refined implicitly due to its relative representation to its source keyframe. We demonstrate the efficiency of the proposed visual SLAM algorithm for applications onboard MAVs in experiments with both autonomous and manual flights. The pose tracking results are compared with the ground truth data provided by an external tracking system.

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“…Keypoints are extracted using SURF. In [7] and [8] the authors propose a visual SLAM algorithm for autonomous navigation in order to search for a predefined landing site based on a known marker. The code runs in real-time using a dual-core CPU, integrating inertial data for reducing geometrical ambiguities.…”

Section: Related Workmentioning

confidence: 99%

Real-time, GPU-based pose estimation of a UAV for autonomous takeoff and landing

Benini

Rutherford

Valavanis

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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This paper proposes a real-time system for pose estimation of an Unmanned Aerial Vehicle (UAV) using parallel image processing of a known marker. The system exploits the capabilities of a high-performance CPU/GPU embedded system in order to provide on-board high-frequency pose estimation, eliminating the need for transmitting the video stream offboard, and enabling autonomous takeoff and landing. The system is evaluated extensively with lab and field tests on board a small quadrotor. The results show that the proposed system is able to provide precise pose estimation with a framerate of at least 30 fps and an image resolution of 640x480 pixels. The use of the GPU for image filtering and marker detection provides an upper bound on the required computation time regardless of the complexity of the image thereby allowing for robust marker detection even in cluttered environments.

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Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision

Cited by 49 publications

References 27 publications

Risk-aware trajectory generation with application to safe quadrotor landing

Risk-aware trajectory generation with application to safe quadrotor landing

Robust Onboard Visual SLAM for Autonomous MAVs

Real-time, GPU-based pose estimation of a UAV for autonomous takeoff and landing

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