Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

Sani, Mohammad Fattahi; Karimian, Ghader

doi:10.1109/iconda.2017.8270408

Cited by 89 publications

(33 citation statements)

References 39 publications

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“…To deal with indoor scenarios, where GPS signal is lost, Sani and Karimian proposed a low-cost navigation and landing solution, tested with the quadrotor Parrot AR.Drone 2.0 [27]. To achieve the required functionalities, they used the values from the Inertial Measurement Unit (IMU) sensor and the images of the camera.…”

Section: Related Workmentioning

confidence: 99%

UAV Landing Using Computer Vision Techniques for Human Detection

Safadinho

Ramos

Ribeiro

et al. 2020

Sensors

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The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed—without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5–10 m, with recalls from 59%–76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

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Section: Related Workmentioning

confidence: 99%

UAV Landing Using Computer Vision Techniques for Human Detection

Safadinho

Ramos

Ribeiro

et al. 2020

Sensors

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“…In fact, as seen in Figure 10, deviations in location 4 (labeled as B) are clearly higher than in location 2 (labeled as A), while the pixelic deviations are similar in both cases. In next section we will introduce these values of pixelic deviations in the expressions in (19) to obtain the variance of X c and compute the fusion estimate as in (24). The choice of deviation values is a key point, as discussed further.…”

Section: Camera Measurementsmentioning

confidence: 99%

“…This kind of approach requires ad-hoc offline processes to collect information about the environment and be stored in large databases [3,[20][21][22]. The artificial landmark approach implies a more invasive strategy but, on the other hand, does not require a priori environment knowledge [23,24]. With respect to the camera location, it is usually placed onboard the mobile agent [3,[20][21][22][23], being a less common practice deploying the cameras in the environment infrastructure (which match better the conception of an intelligent space) [16,25].…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive review where different approaches and their features and performance can be found in [26]. In many works different sensors are used in a cooperative (not fusion) way, as in [20] with a camera and a LIDAR (Laser Imaging Detection and Ranging) or in [27] with camera and odometry, both for robot navigation applications, or [24] where ArUco markers [28] (widely known encoded markers, also used in this work) and an IMU cooperate for a drone navigation and landing application. There is no dominant approach in fusion of cameras with other sensors in positioning and navigation applications.…”

Section: Introductionmentioning

confidence: 99%

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An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Martín-Gorostiza¹,

García-Garrido²,

Pizarro³

et al. 2019

Preprint

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A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements  is between 0.4 cm (fusion) and  2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

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“…Authors in [9][10][11] developed a system, in which an AR.Drone UAV lands on a ground vehicle. Other researchers studied automatic landing of a PID-controlled low-cost quadrotor [12], or used H-shaped markers on pushcart carriers as the landing target [13]. However, the direction of the quadrotor was not considered in these studies.…”

Section: Introductionmentioning

confidence: 99%

Automatic landing of a low-cost quadrotor using monocular vision and Kalmanfilter in GPS-denied environments

Sani¹,

Shoaran²,

Karimian³

2019

Turk J Elec Eng & Comp Sci

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Unmanned aerial vehicles are becoming an important part of the modern life. Despite some recent advances in GPS-aided navigation of quadrotors, the concern of crash and collision still overshadows their reliability and safety, especially in GPS-denied environments. Therefore, the necessity for developing fully automatic methods for safe, accurate, and independent landing of drones increases over time. This paper investigates the autolanding process by focusing on an accurate and continuous position estimation of the drone using a monocular vision system and the fusion with the inertial measurement unit and ultrasonic sensors' data. An ARUCO marker is used as the landing pad, and the information is processed in the ground station through a real-time Wi-Fi link. In order to overcome the closed loop instability caused by the communication and localization delays, we propose a method called "movement slicing method". This method divides the moves around the marker into moving and waiting slices and makes the landing process not only more accurate but also faster. Experimental results show a successful landing of the UAV on a predefined location, while it is accurately aligned with the marker using the proposed method.

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Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

Cited by 89 publications

References 39 publications

UAV Landing Using Computer Vision Techniques for Human Detection

UAV Landing Using Computer Vision Techniques for Human Detection

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

Automatic landing of a low-cost quadrotor using monocular vision and Kalmanfilter in GPS-denied environments

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