Co‐registration of panoramic mobile mapping images and oblique aerial images

Fanta-Jende, P.; Nex, Francesco; Vosselman, G.; Gerke, M.

doi:10.1111/phor.12276

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…In their work, the matching procedure was supported by point clouds generated through dense matching, and feature matching was carried out by SIFT features and a variant of the RANSAC approach. A fully automatic procedure was recently proposed to register panoramic mobile mapping images with oblique aerial imagery (Fanta‐Jende et al., 2019). Their registration procedure took advantage of the planarity of building façades by means of sparse point clouds generated from mobile mapping image triplets.…”

Section: Previous Studiesmentioning

confidence: 99%

A new productive framework for point‐based matching of oblique aircraft and UAV‐based images

Baş

2021

The Photogrammetric Record

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A novel productive framework for point‐based feature matching of oblique aircraft and UAV imagery is presented. The proposed framework makes use of the powerful AKAZE descriptor for feature extraction and an iterative scheme is developed to construct as many tentative matches as possible. During the iterations, cross checks, together with Lowe’s nearest‐next distance ratio test, are used to filter erroneous matches. In order to extract putative matches from the tentative matches, three robust approaches, including graph‐cut RANSAC, are evaluated along with the epipolar constraint enforced between the two datasets. The developed framework was validated using the ISPRS image orientation benchmark dataset and yielded successful results in terms of matching precision, even for some difficult cases. The results also outperformed the results of previously developed approaches in the same context.

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Section: Previous Studiesmentioning

confidence: 99%

A new productive framework for point‐based matching of oblique aircraft and UAV‐based images

Baş

2021

The Photogrammetric Record

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“…A prominent research area, related to heterogenous map merging, deals with cross-view image matching, where aerial view images are matched with data collected on the ground (some examples are [103][104][105]). Yamamoto and Nakagawa [103] consider a problem where 3D LIDAR data and satellite image data must be merged to improve building classification.…”

Section: Other Mapsmentioning

confidence: 99%

“…They then retrieve the k nearest neighbors from the reference buildings using a Siamese network and use an efficient multiple nearest neighbor matching method based on dominant sets to find the nearest neighbors of buildings and match them. Work by Fanta-Jende et al [105] addresses matching of mobile mapping data and aerial images by searching for mutual planes in both images and homogenizing images to achieve pixel-level accuracy. Some methods address the cross-view matching by using semantic information [106,107].…”

Section: Other Mapsmentioning

confidence: 99%

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Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

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Multi-robot mapping and environment modeling have several advantages that makeit an attractive alternative to the mapping with a single robot: faster exploration, higherfault tolerance, richer data due to different sensors being used by different systems. However,the environment modeling with several robotic systems operating in the same area causes problemsof higher-order—acquired knowledge fusion and synchronization over time, revealing the sameenvironment properties using different sensors with different technical specifications. While theexisting robot map and environment model merging techniques allow merging certain homogeneousmaps, the possibility to use sensors of different physical nature and different mapping algorithms islimited. The resulting maps from robots with different specifications are heterogeneous, and eventhough some research on how to merge fundamentally different maps exists, it is limited to specificapplications. This research reviews the state of the art in homogeneous and heterogeneous mapmerging and illustrates the main research challenges in the area. Six factors are identified thatinfluence the outcome of map merging: (1) robotic platform hardware configurations, (2) maprepresentation types, (3) mapping algorithms, (4) shared information between robots, (5) relativepositioning information, (6) resulting global maps.

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“…Whereas GCPs offer a high accuracy but are labour-intensive to acquire and to integrate, maps are generalisations of the real world, difficult to intersect with acquired MM data, and cannot necessarily provide for surveying-grade accuracy. In our previous work, we have presented co-registration approaches for mobile mapping and aerial nadir and oblique images (Fanta-Jende et al, 2019;Jende et al, 2018a;Jende et al, 2018b). Airborne platforms are not affected by the aforementioned GNSS issues, and aerial images can thus be used as a reference.…”

Section: Introductionmentioning

confidence: 99%

Correction of Mobile Mapping Trajectories in GNSS-Denied Environments Using Aerial Nadir and Aerial Oblique Images

Fanta-Jende

Nex

Gerke

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Mobile mapping enables highly accurate as well as high-resolution image data capture at low cost and high speed. As a terrestrial acquisition technique predominately employed in urban, and thus built-up areas, non-line-of-sight and multipath effects challenge its absolute positioning capabilities provided by GNSS. In conjunction with IMU drift, the platform’s trajectory has an unknown accuracy, which influences the quality of the data product. By employing a highly accurate co-registration technique for identifying tie correspondences between mobile mapping images and aerial nadir as well as aerial oblique images, reliable ground control can be introduced into an adjustment solution. We exemplify the performance of our registration results by showcasing adjusted mobile mapping trajectories in four different test areas, each with about 100 consecutive recording locations (approx. 500&thinsp;m length) in the city centre of Rotterdam, The Netherlands. The mobile mapping data has been adjusted in different configurations, i.e. with nadir or oblique aerial correspondences only and if possible in conjunction. To compare the horizontal as well as the vertical accuracy before and after the respective adjustments, more than 30 ground control points were surveyed for these experiments. In general, the aim of our technique is not only to correct mobile mapping trajectories in an automated fashion but also to verify their accuracy without the need to acquire ground control points. In most of our test cases, the overall accuracy of the mobile mapping image positions in the trajectory could be improved. Depending on the test area, an RMSE in 3D between 15 and 21&thinsp;cm and an RMSE in 2D between 11 and 18&thinsp;cm is achievable.</p>

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Co‐registration of panoramic mobile mapping images and oblique aerial images

Cited by 7 publications

References 36 publications

A new productive framework for point‐based matching of oblique aircraft and UAV‐based images

A new productive framework for point‐based matching of oblique aircraft and UAV‐based images

Heterogeneous Map Merging: State of the Art

Correction of Mobile Mapping Trajectories in GNSS-Denied Environments Using Aerial Nadir and Aerial Oblique Images

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