An approach to scene matching algorithm for UAV autonomous navigation

Ao, Tianci; Liu, Xiaodong; Ren, Yan; Luo, Rui; Xi, Jie

doi:10.1109/ccdc.2018.8407275

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Experiment 1: The scenery is that UAV image is parallel to the corresponding Google map or tile. This condition is also verified by Ao's method [2]: it is expected to see that the correct tile could be selected, i.e., the correct scenery is located. Meanwhile, the computation time could be reduced compared with Ao's algorithm.…”

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 59%

“…This paper is organized as follows: in Section II, the basic ideas of SURF and matching algorithm will be reviewed. In Section III, the matching difficulties accompanied by larger scale gap will be re-examined and an alternative method which is proposed by Ao [2] will be briefly discussed. Then a faster matching algorithm method is proposed.…”

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

“…Matching based on speed: if the size of matching database is huge, you may prefer "Approximate" mode rather than "Exhaustive" mode to do matching [4]. In this paper the SAD is preferred and considering the amount of interest points is small the exhaustive mode is selected [2].…”

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

“…Therefore, without any image pre-processing, it is not realistic to match UAV aerial images and the satellite maps by using SURF [15,18]. Ao used to propose a method which traverses a range of ratios to search the optimal scaling ratio [2]. In his algorithm, an initial value scale α 0 is given which starts with the division of UAV flying height 500 meters to satellite vision height 9600 meters, i.e., α 0 = H U AV H satellite = 500 9600 ≈ 0.05.…”

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

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A fast matching algorithm for the images with large scale disparity

Chen¹,

Wang²,

Ren³

2020

Mathematical Foundations of Computing

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With the expansion of application areas of unmanned aerial vehicle (UAV) applications, there is a rising demand to realize UAV navigation by means of computer vision. Speeded-Up Robust Features (SURF) is an ideal image matching algorithm to be applied to solve the location for UAV. However, if there is a large scale difference between two images with the same scene taken by UAV and satellite respectively, it is difficult to apply SURF to complete the accurate image matching directly. In this paper, a fast image matching algorithm which can bridge the huge scale gap is proposed. The fast matching algorithm searches an optimal scaling ratio based on the ground distance represented by pixel. Meanwhile, a validity index for validating the performance of matching is given. The experimental results illustrate that the proposed algorithm performs better performance both on speed and accuracy. What's more, the proposed algorithm can also obtain the correct matching results on the images with rotation. Therefore, the proposed algorithm could be applied to location and navigation for UAV in future.

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Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 59%

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

See 2 more Smart Citations

A fast matching algorithm for the images with large scale disparity

Chen¹,

Wang²,

Ren³

2020

Mathematical Foundations of Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach, unlike traditional digital photogrammetry, resolves the collinearity equations without the need for any control point, providing a sparse point cloud in an arbitrary coordinate system and a full camera calibration [16,17]. This is possible due to image matching algorithms that automatically search for similar image objects, called keypoints, through the analysis of the correspondence, similarity, and consistency of the image features [18]. SfM is paired with multi-view stereopsis (MVS) techniques that apply an expanding procedure of the sparse set of matched keypoints in order to obtain a dense point cloud [19].…”

Section: Introductionmentioning

confidence: 99%

UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

et al. 2020

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Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.

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Influence of AGL flight and off-nadir images on UAV-SfM accuracy in complex morphology terrains

Agüera-Vega

Ferrer-González

Ramírez

et al. 2022

Geocarto International

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In the field of geosciences and engineering, situations arise where special attention have to be paid to the planning of the UAV-photogrammetric project, i.e., terrain with complex geometry and steep slopes. The use of off-nadir imagery and flights at a fixed height above ground level (AGL) are postulated as possible factors to be considered to achieve high accuracies. The objective of this study is to evaluate the influence of image angle, frontal and side overlaps, and type of flight (above mean sea level (AMSL) or AGL), on the accuracy of the dense 3 D point cloud yielded by UAV-SfM. The results obtained showed that imagery with an angle between 20° and 35° in two perpendicular flight line directions, 90/90, 90/70 or 70/70 overlap and AGL flight is the optimal combination for best accuracy and high precision. With nadir imagery, the combination of factors that gives the most accuracy results are AGL flights with a 90/70 overlap.

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An approach to scene matching algorithm for UAV autonomous navigation

Cited by 4 publications

References 9 publications

A fast matching algorithm for the images with large scale disparity

A fast matching algorithm for the images with large scale disparity

UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

Influence of AGL flight and off-nadir images on UAV-SfM accuracy in complex morphology terrains

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