Efficient and Robust Large-Scale Rotation Averaging

Chatterjee, A.; Govindu, Venu Madhav

doi:10.1109/iccv.2013.70

Cited by 230 publications

(278 citation statements)

References 16 publications

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“…Thus, methods have been sought after to globally average pairwise camera rotations in a robust way (Hartley et al, 2011, Chatterjee andGovindu, 2013). These methods rely on image features only for the robust computation of relative image orientations.…”

Section: Handling Large Bundle Blocksmentioning

confidence: 99%

Efficient Orientation and Calibration of Large Aerial Blocks of Multi-Camera Platforms

Karel¹,

Ressl²,

Pfeifer³

2016

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

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KEY WORDS: multi-sensor calibration, oblique aerial imagery, tie point decimation, bundle block adjustment, affine-invariant feature point ABSTRACT:Aerial multi-camera platforms typically incorporate a nadir-looking camera accompanied by further cameras that provide oblique views, potentially resulting in utmost coverage, redundancy, and accuracy even on vertical surfaces. However, issues have remained unresolved with the orientation and calibration of the resulting imagery, to two of which we present feasible solutions. First, as standard feature point descriptors used for the automated matching of homologous points are only invariant to the geometric variations of translation, rotation, and scale, they are not invariant to general changes in perspective. While the deviations from local 2D-similarity transforms may be negligible for corresponding surface patches in vertical views of flat land, they become evident at vertical surfaces, and in oblique views in general. Usage of such similarity-invariant descriptors thus limits the amount of tie points that stabilize the orientation and calibration of oblique views and cameras. To alleviate this problem, we present the positive impact on image connectivity of using a quasi affine-invariant descriptor. Second, no matter which hard-and software are used, at some point, the number of unknowns of a bundle block may be too large to be handled. With multi-camera platforms, these limits are reached even sooner. Adjustment of sub-blocks is sub-optimal, as it complicates data management, and hinders self-calibration. Simply discarding unreliable tie points of low manifold is not an option either, because these points are needed at the block borders and in poorly textured areas. As a remedy, we present a straight-forward method how to considerably reduce the number of tie points and hence unknowns before bundle block adjustment, while preserving orientation and calibration quality.

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Section: Handling Large Bundle Blocksmentioning

confidence: 99%

Efficient Orientation and Calibration of Large Aerial Blocks of Multi-Camera Platforms

Karel¹,

Ressl²,

Pfeifer³

2016

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

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“…Even if the size of this linear system is proportional to the number of absolute transformations, its sparsity is usually exploited in solvers such as g 2 o, resulting in an algorithm with a reasonable computational time for a small or medium sized problem. Using this formalism, several algorithms have been recently proposed to perform robust motion averaging (i.e when loop closures contain erroneous measurements): [1] and [8] proposed re-weighted schemes; [28] and [10] introduced auxiliary variables that in fact correspond to using a robust kernel as it was recently shown in [30] in the context of bundle adjustment; [22] proposed a consensus based algorithm which optimizes clusters of loop closures with a GN and checks their consistency with statistical tests. None of these approaches fulfill our first two requirements (Computational efficiency and Memory efficiency).…”

Section: Related Workmentioning

confidence: 99%

“…More specifically, at time instant k − 1, the posterior distribution of the state is assumed to have the following factorized form: (8) where…”

Section: Estimated Statementioning

confidence: 99%

“…The motion averaging problem, also called "multiple rotation averaging" when dealing with 3D rotations or "pose-graph inference" when applied to camera poses, has been studied for more than fifteen years [16,17,14,18,6,31,19,32,24] and is still a very active area of research [20,4,8,29,7,12,10,5]. This generic problem arises in a large number of applications, such as video mosaicking [6,24], reconstruction of 3D scenes [27,10] or visual SLAM [14,13], where only the considered group of transformations changes: SE(3) for 3D euclidean motions, SL(3) for homographies, Sim(3) for 3D similarities.…”

Section: Introductionmentioning

confidence: 99%

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Online Variational Bayesian Motion Averaging

Bourmaud

2016

Computer Vision – ECCV 2016

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Abstract. In this paper, we propose a novel algorithm dedicated to online motion averaging for large scale problems. To this end, we design a filter that continuously approximates the posterior distribution of the estimated transformations. In order to deal with large scale problems, we associate a variational Bayesian approach with a relative parametrization of the absolute transformations. Such an association allows our algorithm to simultaneously possess two features that are essential for an algorithm dedicated to large scale online motion averaging: 1) a low computational time, 2) the ability to detect wrong loop closure measurements. We extensively demonstrate on several applications (binocular SLAM, monocular SLAM and video mosaicking) that our approach not only exhibits a low computational time and detects wrong loop closures but also significantly outperforms the state of the art algorithm in terms of RMSE.

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“…The Weiszfeld algorithm under 1 norm is applied in (Hartley et al, 2011) to increase robustness. (Chatterjee & Govindu., 2013) refine their 1 solution in an iteratively reweighted least squares process in which they incorporate the Huber estimator (Huber, 1964), i.e. an M-estimator.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Structure From Motion Combining Global Image Orientation and Structureless Bundle Adjustment

Cefalu

Haala

Fritsch

2017

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

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ABSTRACT:Global image orientation techniques aim at estimating camera rotations and positions for a whole set of images simultaneously. One of the main arguments for these procedures is an improved robustness against drifting of camera stations in comparison to more classical sequential approaches. Usually, the process consists of computation of absolute rotations and, in a second step, absolute positions for the cameras. Either the first or both steps rely on the network of transformations arising from relative orientations between cameras. Therefore, the quality of the obtained absolute results is influenced by tensions in the network. These may e.g. be induced by insufficient knowledge of the intrinsic camera parameters. Another reason can be found in local weaknesses of image connectivity. We apply a hierarchical approach with intermediate bundle adjustment to reduce these effects. We adopt efficient global techniques which register image triplets based on fixed absolute camera rotations and scaled relative camera translations but do not involve scene structure elements in the fusion step. Our variant employs submodels of arbitrary size, orientation and scale, by computing relative rotations and scales between -and subsequently absolute rotations and scales for -submodels and is applied hierarchically. Furthermore we substitute classical bundle adjustment by a structureless approach based on epipolar geometry and augmented with a scale consistency constraint.

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Efficient and Robust Large-Scale Rotation Averaging

Cited by 230 publications

References 16 publications

Efficient Orientation and Calibration of Large Aerial Blocks of Multi-Camera Platforms

Efficient Orientation and Calibration of Large Aerial Blocks of Multi-Camera Platforms

Online Variational Bayesian Motion Averaging

Hierarchical Structure From Motion Combining Global Image Orientation and Structureless Bundle Adjustment

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