Sparse Sparse Bundle Adjustment

Konolige, Kurt

doi:10.5244/c.24.102

Cited by 128 publications

(90 citation statements)

References 15 publications

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“…We remark that Schur complement is a common tool in computer vision [22], [20]. However, smart factors do more than Schur complement: they are able to manage degenerate problems, exploiting domain-specific knowledge.…”

Section: A Elimination In Linear(ized) Factor Graphsmentioning

confidence: 99%

Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors

Carlone

Kira

Beall

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Factor graphs are a general estimation framework that has been widely used in computer vision and robotics. In several classes of problems a natural partition arises among variables involved in the estimation. A subset of the variables are actually of interest for the user: we call those target variables. The remaining variables are essential for the formulation of the optimization problem underlying maximum a posteriori (MAP) estimation; however these variables, that we call support variables, are not strictly required as output of the estimation problem. In this paper, we propose a systematic way to abstract support variables, defining optimization problems that are only defined over the set of target variables. This abstraction naturally leads to the definition of smart factors, which correspond to constraints among target variables. We show that this perspective unifies the treatment of heterogeneous problems, ranging from structureless bundle adjustment to robust estimation in SLAM. Moreover, it enables to exploit the underlying structure of the optimization problem and the treatment of degenerate instances, enhancing both computational efficiency and robustness.

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Section: A Elimination In Linear(ized) Factor Graphsmentioning

confidence: 99%

Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors

Carlone

Kira

Beall

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…This leads to a robust optimization task and an expected improvement of the camera pose path. One of the most successful algorithms is the "sparse bundle adjustment" (Konolige, 2010). The bundle adjustment procedure can be part of the transformation estimation step, or it can be a separate post-processing step.…”

Section: Loop Closurementioning

confidence: 99%

Efficient generation of 3D surfel maps using RGB–D sensors

Wilkowski

Kornuta

Stefańczyk

et al. 2016

International Journal of Applied Mathematics and Computer Science

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The article focuses on the problem of building dense 3D occupancy maps using commercial RGB-D sensors and the SLAM approach. In particular, it addresses the problem of 3D map representations, which must be able both to store millions of points and to offer efficient update mechanisms. The proposed solution consists of two such key elements, visual odometry and surfel-based mapping, but it contains substantial improvements: storing the surfel maps in octree form and utilizing a frustum culling-based method to accelerate the map update step. The performed experiments verify the usefulness and efficiency of the developed system.

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“…This non linear least weighted square problem can be efficiently resolved through several numerical implementations rooted in Gauss-Newton methods, that conveniently exploit the structure of the Jacobian associated to Equation (16), see e.g. (Agarwal et al, 2010, Konolige andGarage, 2010) for a review and a more detailed discussion.…”

Section: Bundle Adjustment With Planar Constraintmentioning

confidence: 99%

Bending the Doming Effect in Structure From Motion Reconstructions Through Bundle Adjustment

Magri

Toldo²

2017

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

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ABSTRACT:Structure from Motion techniques provides low-cost and flexible methods that can be adopted in arial surveying to collect topographic data with accurate results. Nevertheless, the so-called "doming effect", due to unfortunate acquisition conditions or unreliable modeling of radial distortion, has been recognized as a critical issue that disrupts the quality of the attained 3D reconstruction. In this paper we propose a novel method, that works effectively in the presence of a nearly flat soil, to tackle a posteriori the doming effect: an automatic ground detection method is used to capture the doming deformation flawing the reconstruction, which in turn is wrapped to the correct geometry by iteratively enforcing a planarity constraint through a Bundle Adjustment framework. Experiments on real word datasets demonstrate promising results.

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Sparse Sparse Bundle Adjustment

Cited by 128 publications

References 15 publications

Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors

Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors

Efficient generation of 3D surfel maps using RGB–D sensors

Bending the Doming Effect in Structure From Motion Reconstructions Through Bundle Adjustment

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