Associating Uncertainty With Three-Dimensional Poses for Use in Estimation Problems

By T. Barfoot, Paul Timothy Furgale. IEEE Transactions on robotics, 2014

338 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/84c6c752e9295a39544ab2ecb2d360ae820e9ab1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Contact-aided invariant extended Kalman filtering for robot state estimation (2019, background): “This type of distribution can often capture the underlying system uncertainty better than a standard Gaussian defined in Euclidean space (Barfoot and Furgale, 2014; Long et al., 2013).”
Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation (2015, positive): “48) This property follows directly from the Baker–Campbell–Hausdor↵ (BCH) formula under the assumption that is small [1].”
Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards (2016, positive): “ξ = exp (υ)μξ, where the ∧ operator maps from R(6) to the Lie algebra se(3) [3].”
A micro Lie theory for state estimation in robotics (2018, methodology): “While [6] and us use local perturbations X = X̄ ⊕ Xτ , [5], [10] use perturbations around the origin, X = Eτ⊕X̄ , yielding global covariance specifications.”
State Estimation for Robotics (2017, background): “We show that for linear-Gaussian systems, the discrete-time formulation is implementing the continuous-time one exactly, under certain special conditions (Tong et al., 2013; Barfoot et al., 2014).”
PROBE-GK: Predictive robust estimation using generalized kernels (2016, methodology): “Frobenius norm (as defined in [6]) between the covariances estimated by PROBE-GK and the true covariances for a test trial.”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, background): “in Section III-C. B. Uncertainty Description in SO(3) A natural deﬁnition of uncertainty in SO(3) is to deﬁne a distribution in the tangent space, and then map it to SO(3) via the exponential map (6) [44, 46, 47]: ~R = R Exp(); ˘N(0;) ; (12) where R is a given noise-free rotation (the mean) and is a”