High-precision, consistent EKF-based visual-inertial odometry

Li, Mingyang; Mourikis, Anastasios I.

doi:10.1177/0278364913481251

Cited by 691 publications

(585 citation statements)

References 47 publications

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“…The fundamental idea behind feature-based approaches (both filtering-based [15,19] and keyframe-based [15]) is to split the overall problem -estimating geometric information from images -into two sequential steps: First, a set of feature observations is extracted from the image. Second, the camera position and scene geometry is computed as a function of these feature observations only.…”

Section: Related Workmentioning

confidence: 99%

LSD-SLAM: Large-Scale Direct Monocular SLAM

Engel¹,

Schöps²,

Cremers³

2014

Lecture Notes in Computer Science

2,737

1,974

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Abstract. We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment, the 3D environment is reconstructed in real-time as pose-graph of keyframes with associated semi-dense depth maps. These are obtained by filtering over a large number of pixelwise small-baseline stereo comparisons. The explicitly scale-drift aware formulation allows the approach to operate on challenging sequences including large variations in scene scale. Major enablers are two key novelties: (1) a novel direct tracking method which operates on sim(3), thereby explicitly detecting scale-drift, and (2) an elegant probabilistic solution to include the effect of noisy depth values into tracking. The resulting direct monocular SLAM system runs in real-time on a CPU.

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Section: Related Workmentioning

confidence: 99%

LSD-SLAM: Large-Scale Direct Monocular SLAM

Engel¹,

Schöps²,

Cremers³

2014

Lecture Notes in Computer Science

2,737

1,974

View full text Add to dashboard Cite

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“…Refinements to the MSC-KF have improved filter consistency by restricting the state updates to respect the unobservable nullspace of the system, see Huang, Hesch and Li et al [90,98,138], which enters the state through linearization assumptions and numerical computational issues. Filtering style visual inertial navigation system (VINS) generally estimate a single snapshot estimate of inertial sensor biases.…”

Section: Tightly Coupled Visual-inertial (Msc-kf)mentioning

confidence: 99%

Multi-modal and inertial sensor solutions for navigation-type factor graphs

Fourie¹

2017

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This thesis presents a sum-product inference algorithm for platform navigation called Multi-modal iSAM (incremental smoothing and mapping). Common Gaussianonly likelihoods are restrictive and require a complex front-end processes to deal with non-Gaussian measurements. Instead, our approach allows the front-end to defer ambiguities with non-Gaussian measurement models. We retain the acyclic Bayes tree (and incremental update strategy) from the predecessor iSAM2 maxproduct algorithm [Kaess et al., IJRR 2012]. The approach propagates continuous beliefs on the Bayes (Junction) tree, which is an efficient symbolic refactorization of the nonparametric factor graph, and asymptotically approximates the underlying Chapman-Kolmogorov equations. Our method tracks dominant modes in the marginal posteriors of all variables with minimal approximation error, while suppressing almost all low likelihood modes (in a non-permanent manner). Keeping with existing inertial navigation, we present a novel, continuous-time, retroactively calibrating inertial odometry residual function, using preintegration to seamlessly incorporate pure inertial sensor measurements into a factor graph. We centralize around a factor graph (with starved graph databases) to separate elements of the navigation into an ecosystem of processes. Practical examples are included, such as how to infer multi-modal marginal posterior belief estimates for ambiguous loop closures; raw beam-formed acoustic measurements; or conventional parametric likelihoods, and others.

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“…Therefore, we compute {R i,i+1 } from (19) and backsubstitute the optimal value in the expression of the factor. This problem has been explored with a different application in [6] and [27].…”

Section: Appendixmentioning

confidence: 99%

“…As in the linear case, we obtain our smart factor by plugging back the optimal solution {R i,i+1 } (which is function of R u to R v ) into (19):…”

Section: Appendixmentioning

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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High-precision, consistent EKF-based visual-inertial odometry

Cited by 691 publications

References 47 publications

LSD-SLAM: Large-Scale Direct Monocular SLAM

LSD-SLAM: Large-Scale Direct Monocular SLAM

Multi-modal and inertial sensor solutions for navigation-type factor graphs

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

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