Bundle adjustment without iterative structure estimation and its application to navigation

Indelman, Vadim

doi:10.1109/plans.2012.6236952

Cited by 13 publications

(29 citation statements)

References 13 publications

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“…It is worth mentioning that the presented probabilistic analysis is valid for other existing structureless BA methods [18,15,4] as well.…”

Section: Probabilistic Analysismentioning

confidence: 88%

“…Another family of recently suggested methods is "structure-less" BA [18,15,4,6], in which the camera poses are optimized without including structure parameters into the iterative optimization procedure. In structure-less BA, the 3D points are algebraically eliminated using multiview constraints and the optimization minimizes the errors in satisfying these constraints, as opposed to optimizing the re-projection errors in conventional BA.…”

Section: Introductionmentioning

confidence: 99%

“…The first structure-less BA method was introduced by Steffen et al [18] who optimized the corrections of image observations subject to satisfying trifocal tensor constraints [2]. A similar concept was developed in [4] using threeview constraints [5] instead of the trifocal tensor. Rodríguez et al [15] obtained a reduced computational complexity by reformulating the optimized cost function and refraining from correcting the pixels.…”

Section: Introductionmentioning

confidence: 99%

“…The developed method, called incremental light bundle adjustment (iLBA), adaptively identifies which camera poses should be optimized each time a new camera is incorporated into the optimization. Typically, incremental optimization involves only a small number of cameras as opposed to always optimizing all cameras in previous structure-less BA methods [18,15,4]. iLBA utilizes three-view constraints, which in contrast to using only epipolar constraints in structure-less BA [15] , allow consistent motion estimates even when the camera centers are co-linear.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic analysis of incremental light bundle adjustment

Indelman

Roberts

Dellaert

2013

2013 IEEE Workshop on Robot Vision (WORV)

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This paper presents a probabilistic analysis of the recently introduced incremental light bundle adjustment method (iLBA) [6]. In iLBA, the observed 3D points are algebraically eliminated, resulting in a cost function with only the camera poses as variables, and an incremental smoothing technique is applied for efficiently processing incoming images. While we have already showed that compared to conventional bundle adjustment (BA), iLBA yields a significant improvement in computational complexity with similar levels of accuracy, the probabilistic properties of iLBA have not been analyzed thus far. In this paper we consider the probability distribution that corresponds to the iLBA cost function, and analyze how well it represents the true density of the camera poses given the image measurements. The latter can be exactly calculated in bundle adjustment (BA) by marginalizing out the 3D points from the joint distribution of camera poses and 3D points. We present a theoretical analysis of the differences in the way that LBA and BA use measurement information. Using indoor and outdoor datasets we show that the first two moments of the iLBA and the true probability distributions are very similar in practice.

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“…It is worth mentioning that the presented probabilistic analysis is valid for other existing structureless BA methods [18,15,4] as well.…”

Section: Probabilistic Analysismentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic analysis of incremental light bundle adjustment

Indelman

Roberts

Dellaert

2013

2013 IEEE Workshop on Robot Vision (WORV)

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“…In (Steffen et al, 2010) epipolar constraints are combined with trifocal constraints to overcome the problem of distance ambiguity for collinear camera stations which is induced by the exclusive use of epipolar constraints. (Indelman, 2012a, Indelman et al, 2012b) derives a scale consistency constraint from addition of scaled observation vectors and camera baselines in a three-view scenario by reformulating rank conditions of the equation system. (Rodriguez et al, 2011a(Rodriguez et al, , 2011b and (Indelman et al, 2012b) demonstrate that accurate solutions can be achieved without introduction of additional unknowns, which is also the case for our implementations (Cefalu et al, 2016).…”

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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