Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

Lupton, Todd; Sukkarieh, Salah

doi:10.1109/tro.2011.2170332

Cited by 407 publications

(320 citation statements)

References 16 publications

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“…However, these have focused on the simplified problem of estimating the 2D robot pose since the number of particles required is exponential in the size of the state vector. Existing work has addressed a variety of issues in VINS, such as reducing its computational cost [26,37], dealing with delayed measurements [36], increasing the accuracy of feature initialization and estimation [15], and improving the robustness to estimator initialization errors [21].…”

Section: Introductionmentioning

confidence: 99%

Towards Consistent Vision-Aided Inertial Navigation

Hesch

Kottas

Bowman

et al. 2013

Springer Tracts in Advanced Robotics

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In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS) from a standpoint of system observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, resulting in smaller uncertainties, larger estimation errors, and possibly even divergence. We develop an Observability-Constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. Our analysis, along with the proposed method for reducing inconsistency, are extensively validated with simulation trials and real-world experiments.

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Section: Introductionmentioning

confidence: 99%

Towards Consistent Vision-Aided Inertial Navigation

Hesch

Kottas

Bowman

et al. 2013

Springer Tracts in Advanced Robotics

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“…However, the rapid increase in the number of poses makes it difficult to compute real-time solutions for large problems. Their later work [103] adopted the preintegrals of Lupton [143], but again did not present an analytical version of the inertial sensor model they employed.…”

Section: Adding Inertial Measurements To Factor Graphsmentioning

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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“…This idea appeared several times in literature [23], [24], [25], (while to the best of our knowledge the corresponding elimination in rotation graphs has not been investigated). In particular, in [25] intermediate pose relations (computed from an IMU sensor) are "summarized" or preintegrated into a single constraint. Our C++ implementation of the preintegrated IMU factors, and a Matlab example on the Kitti dataset [21] are available on the website https://borg.cc.gatech.edu/.…”

Section: Rumentioning

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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Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

Cited by 407 publications

References 16 publications

Towards Consistent Vision-Aided Inertial Navigation

Towards Consistent Vision-Aided Inertial Navigation

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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