Vision based autonomous landing of an Unmanned Aerial Vehicle on a stationary target

Sudevan, Vidya; Shukla, Amit; Karki, Hamad

doi:10.23919/iccas.2017.8204466

Cited by 25 publications

(9 citation statements)

References 10 publications

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“…First, the following experiment is used to verify the IAEC algorithm. Five desired waypoints, [2,1,2], [3,5,3], [5,2,5], [10,8,4], and [12,2,5], are given in this experiment. In addition, the velocities and accelerations at the start and end points are zero.…”

Section: Landmarksmentioning

confidence: 99%

“…Generally, the 2D landmark is arranged on the top of the moving platform, so its size is often strictly limited. To provide a large range of localization data under this limitation, researchers have adopted landmarks such as 2D codes, ring landmarks, or character landmarks [8][9][10]. Although interesting results have been achieved, they are not necessarily applicable to dynamically moving targets in an open outdoor environment…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Xing

Pan

Feng

et al. 2019

International Journal of Aerospace Engineering

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In the existing vision-based autonomous landing systems for micro aerial vehicles (MAVs) on moving platforms, the limited range of landmark localization, the unknown measurement bias of the moving platform (such as wheel-slip or inaccurate calibration of encoders), and landing trajectory knotting seriously affect system performance. To overcome the above shortcomings, an autonomous landing system using a composite landmark is proposed in this paper. In the proposed system, a notched ring landmark and two-dimensional landmark are combined as an R2D landmark to provide visual localization over a wide range. In addition, the wheel-slip and imprecise calibration of encoders are modeled as the unknown measurement bias of the encoders and estimated online via an extended Kalman filter. The landing trajectory is planned by a solver as a convex quadratic programming problem in each control cycle. Meanwhile, an iterative algorithm for adding equality constraints is proposed and used to verify whether the planned trajectory is feasible or not. The simulation and actual landing experiment results verify the following: the visual localization with the R2D landmark has the advantages of wide localization range and high localization accuracy, the pose estimation result of the moving platform with unknown encoder measurement bias is continuous and accurate, and the proposed landing trajectory planning algorithm provides a continuous trajectory for reliable landing.

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Section: Landmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Xing

Pan

Feng

et al. 2019

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…The relative pose between the current and the previous frames could be tracked by observing a structured-unknown object. [8][9][10] Based on speeded-up robust features (SURF) feature descriptors and fast approximate nearest neighbor search (FLANN) matcher, a template matching method 11 was presented to determine the relative position of the landing target. Also, artificial neural networks (ANN) have been employed to estimate the state of the landing UAV in Moriarty et al 12 Similar to our work, Araar et al 13 have designed an adequate pad to extend the detection range.…”

Section: Previous Workmentioning

confidence: 99%

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Yuan

Xiao

Xiu

et al. 2018

International Journal of Distributed Sensor Networks

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The vision-based localization of rotor unmanned aerial vehicles for autonomous landing is challenging because of the limited detection range. In this article, to extend the vision detection and measurement range, a hierarchical vision-based localization method is proposed for unmanned aerial vehicle autonomous landing. In such a hierarchical framework, the landing is defined into three phases: “Approaching,”“Adjustment,” and “Touchdown,” in which visual artificial features at different scales can be detected from the designed object pattern for unmanned aerial vehicle pose recovery. The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. The results show that our hierarchical vision-based localization has the ability to a consecutive unmanned aerial vehicle localization in a wider working range from far to near, which is significant for autonomous landing.

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“…Ye et al [24] proposed a novel field scene recognition algorithm based on the combination of local pyramid feature and convolutional neural network (CNN) learning feature to identify the complex and autonomous landing scene for the low-small-slow UAV. For identifying the autonomous landing of an UAV on a stationary target, Sudevan et al [25] used the speeded up robust features (SURF) method to detect and compute the key-point descriptors. The fast-approximate nearest neighbor search library (FLANN) was utilized to achieve the accurate matching of the template image with the captured images to determine the target position.…”

Section: Introductionmentioning

confidence: 99%

Precision Landing Test and Simulation of the Agricultural UAV on Apron

Guo

Liu

et al. 2020

Sensors

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Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

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Vision based autonomous landing of an Unmanned Aerial Vehicle on a stationary target

Cited by 25 publications

References 10 publications

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

Autonomous Landing of a Micro Aerial Vehicle on a Moving Platform Using a Composite Landmark

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Precision Landing Test and Simulation of the Agricultural UAV on Apron

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