Vision-based autonomous quadrotor landing on a moving platform

Falanga, Davide; Zanchettin, Alessio; Simovic, Alessandro; Delmerico, Jeffrey A.; Scaramuzza, Davide

doi:10.1109/ssrr.2017.8088164

Cited by 139 publications

(80 citation statements)

References 27 publications

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“…From other MBZIRC participants, we want to cite the early work by the team of the Korea Advanced Institute of Science and Technology (H. Lee, Jung, & Shim, ) where landing on a larger platform at a velocity of 0.75 m/s was demonstrated, but the visual detection was still simplified by a marker reflecting infrared light. Another contribution from a challenger team is Falanga, Zanchettin, Simovic, Delmerico, and Scaramuzza (). Their system lands successfully on a platform moving with 1.5 m/s.…”

Section: Related Workmentioning

confidence: 99%

“…Another contribution from a challenger team is Falanga, Zanchettin, Simovic, Delmerico, and Scaramuzza (2017). Their system lands successfully on a platform moving with 1.5 m/s.…”

Section: Related Workmentioning

confidence: 99%

“…Camera Approach ETH Zürich (Bähnemann, Pantic, et al, 2017) 752 × 480 @ 50 Hz One blob detector for platform, another detector for cross of pattern, tracker uses allocentric positional distribution over figure eight track University of Prague (Báča et al, 2017) 752 × 480 @ 50 Hz Full image adaptive threshold using altitude, circle detection, morphological operations to recognize four equally sized quadrants around the cross University of Zürich (Falanga et al, 2017) In our work, we rely solely on GPS readings-which are considered to have sufficient relative accuracy between the MAVs in an outdoor scenario-and separation of the MAV working areas.…”

Section: Teammentioning

confidence: 99%

See 2 more Smart Citations

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Beul

Nieuwenhuisen

Quenzel

et al. 2018

Journal of Field Robotics

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The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Another contribution from a challenger team is Falanga, Zanchettin, Simovic, Delmerico, and Scaramuzza (2017). Their system lands successfully on a platform moving with 1.5 m/s.…”

Section: Related Workmentioning

confidence: 99%

Section: Teammentioning

confidence: 99%

See 1 more Smart Citation

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Beul

Nieuwenhuisen

Quenzel

et al. 2018

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…The team from the University of Catania (Canteli et al, ) uses tracking learning detection (Kalal, Mikolajczyk, & Matas, ) algorithm processing an image from a fish‐eye camera. The vision system of the University of Zurich team (Falanga, Zanchettin, Simovic, Delmerico, & Scaramuzza, ) combines two standard FOV cameras: one looking downward and second angled down by

4 5^{\circ}

used for visual odometry. Their detection algorithm, which combines thresholding with segmentation, can process the captured images in

12 normalm normals \pm 5.7 normalm normals

.…”

Section: Methodsmentioning

confidence: 99%

Vision techniques for on‐board detection, following, and mapping of moving targets

Štěpán

Krajník

Petrlík

et al. 2018

Journal of Field Robotics

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This study presents computer vision modules of a multi‐unmanned aerial vehicle (UAV) system, which scored gold, silver, and bronze medals at the Mohamed Bin Zayed International Robotics Challenge 2017. This autonomous system, which was running completely on board and in real time, had to address two complex tasks in challenging outdoor conditions. In the first task, an autonomous UAV had to find, track, and land on a human‐driven car moving at 15 km/hr on a figure‐eight‐shaped track. During the second task, a group of three UAVs had to find small colored objects in a wide area, pick them up, and deliver them into a specified drop‐off zone. The computer vision modules presented here achieved computationally efficient detection, accurate localization, robust velocity estimation, and reliable future position prediction of both the colored objects and the car. These properties had to be achieved in adverse outdoor environments with changing light conditions. Lighting varied from intense direct sunlight with sharp shadows cast over the objects by the UAV itself, to reduced visibility caused by overcast to dust and sand in the air. The results presented in this paper demonstrate good performance of the modules both during testing, which took place in the harsh desert environment of the central area of United Arab Emirates, as well as during the contest, which took place at a racing complex in the urban, near‐sea location of Abu Dhabi. The stability and reliability of these modules contributed to the overall result of the contest, where our multi‐UAV system outperformed teams from world’s leading robotic laboratories in two challenging scenarios.

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“…Based on the previous paper, authors in [2] describes the tracking guidance for autonomous drone landing and the vision-based detection of the marker on a moving vehicle with a real-time image processing system. Falanga et al [3] presents a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. This paper relies on computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Yang

Zhang

et al. 2018

Electronics

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Abstract:With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance.

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Vision-based autonomous quadrotor landing on a moving platform

Cited by 139 publications

References 27 publications

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

Vision techniques for on‐board detection, following, and mapping of moving targets

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

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