Structure-from-motion for MAV image sequence analysis with photogrammetric applications

Schönberger, Johannes L.; Fraundorfer, Friedrich; Frahm, Jan

doi:10.5194/isprsarchives-xl-3-305-2014

Cited by 24 publications

(16 citation statements)

References 35 publications

(38 reference statements)

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“…But is computationally expensive requiring O((N c + N 3D ) 3 ) operations for N c cameras or views and N 3D structure, 3D scene feature or tie points (cubic in time and quadratic in memory). Conventional BA shows satisfactory convergence if sufficiently accurate initial estimates are provided either from image feature matching-based essential matrix estimation [23,1] or combined with on-board sensor measurements [29,15,45].…”

Section: Introductionmentioning

confidence: 94%

“…We show that weak camera parameters provided by inaccurate sensors on airborne platforms can be directly used for BA as initial values (and not as extra constraints [29,15,45]), provided that a proper robust function is used. It will be shown that there is no need to apply a camera estimation method (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The camera path and pose information is used to support robust, real-time 3D scene reconstruction using Structurefrom-Motion algorithms (SfM) [24,27,43,45,6,5,7] and direct geo-referencing. Irschara et al in [24] observe that, "These systems rely on highly accurate geo-referencing devices that are calibrated and the delivered pose and orientation estimates are often superior to the one obtained by image based methods (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Fast Structure from Motion for Sequential and Wide Area Motion Imagery

Aliakbarpour

Palaniappan

Seetharaman

2015

2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

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We present a fast and efficient Structure-from-Motion (SfM) pipeline for refinement of camera parameters and 3D scene reconstruction given initial noisy camera metadata measurements. Examples include aerial Wide Area Motion Imagery (WAMI) which is typically acquired in a circular trajectory and other sequentially ordered multiview stereo imagery like Middlebury [46], Fountain [50] or body-worn videos [27]. Image frames are assumed (partially) ordered with approximate camera position and orientation information available from (imprecise) IMU and GPS sensors. In the proposed BA4S pipeline the noisy camera parameters or poses are directly used in a fast Bundle Adjustment (BA) optimization. Since the sequential ordering of the cameras is known, consecutive frame-to-frame matching is used to find a set of feature correspondences for the triangulation step of SfM. These putative correspondences are directly used in the BA optimization without any early-stage filtering (i.e. no RANSAC) using a statistical robust error function based on co-visibility, to deal with outliers (mismatches), which significantly speeds up our SfM pipeline by more than 100 times compared to VisualSfM.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Structure from Motion for Sequential and Wide Area Motion Imagery

Aliakbarpour

Palaniappan

Seetharaman

2015

2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

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“…In addition, the UAV pre-processing procedures, i.e. image stitching (Li, Hui et al 2015;Xu et al 2016; and 3D reconstruction (Turner, Lucieer, and Christopher 2012;Schönberger, Fraundorfer, and Frahm 2014), output extremely large-scale Digital Orthophoto Map (panorama or DOM) with VHR. The panorama or DOM can provide a global and detailed inspection of the investigative area.…”

Section: Motivation and Objectivementioning

confidence: 99%

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Wang

Bai

et al. 2018

Geo-spatial Information Science

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c state Key laboratory for information engineering in surveying, mapping and remote sensing (liesmars), Wuhan University, Wuhan, china ABSTRACT Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

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“…However, Sun et al (2016) parameterize oblique camera poses with nadir camera poses as well as constant relative poses between oblique and nadir cameras, so that the number of unknown parameters and the dimension of the normal equations decrease, which dramatically reduces the computational complexity and memory cost. Relative observations that relate the position and attitude parameters of two consecutive epochs are exploited by Rehak (2017) as well as by Schönberger et al (2014) in the UAV case. Klingner et al (2013) optimize trajectories from Google Street View cars by leveraging precise relative poses from IMU.…”

Section: Introductionmentioning

confidence: 99%

Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

Cavegn

Blaser

Nebiker

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2-3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

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Structure-from-motion for MAV image sequence analysis with photogrammetric applications

Cited by 24 publications

References 35 publications

Fast Structure from Motion for Sequential and Wide Area Motion Imagery

Fast Structure from Motion for Sequential and Wide Area Motion Imagery

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

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