State Estimation for Robotics

By T. Barfoot. 2017

797 citations recorded by the source corpus.

A key aspect of robotics today is estimating the state (e.g., position and orientation) of a robot, based on noisy sensor data. This book targets students and practitioners of robotics by presenting classical state estimation methods (e.g., the Kalman filter) but also important modern topics such as batch estimation, Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. Since most robots operate in a three-dimensional world, common sensor models (e.g., camera, laser rangefinder) are provided followed by practical advice on how to carry out state estimation for rotational state variables. The book covers robotic applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Highlights of this expanded second edition include a new chapter on variational inference, a new section on inertial navigation, more introductory material on probability, and a primer on matrix calculus.

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

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Citations in the corpus

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, neutral): “Bayesian ﬁltering (Candy, 2009) has been extensively used within the domain of time series prediction, with numerous applications across different ﬁelds – including target tracking (Haug, 2012), robotics (Barfoot, 2017), ﬁnance (Ghosh et al., 2015), and medicine (Sukkar et al., 2012).”
Using Inertial Sensors for Position and Orientation Estimation (2017, positive): “3). More specically, SO(3) is a so-called matrix Lie group. For a discussion on the properties of matrix 21 Lie groups and on the reasons why SO(3) is indeed such a group we refer the reader to e.g. [8]. Since rotations are a matrix Lie group, there exists an exponential map from a corresponding Lie”
A micro Lie theory for state estimation in robotics (2018, result): “Our effort is in line with other recent works on the subject [4], [5], [6], which have also identified this need of bringing the LT closer to the robotician.”
Contact-aided invariant extended Kalman filtering for robot state estimation (2019, background): “Γ 0 ( φ ) is simply the exponential map of SO(3) , while Γ 1 ( φ ) is also known as the left Jacobian of SO(3) [27, 6].”
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors (2017, background): “(3) B. Modeling Noise and Optimization on Matrix Lie Groups We model the uncertainty in SO(3) by deﬁning a noise distribution in the tangent space and then mapping it to SO(3) via the exponential map [22], [14]; R~ = RExp() and ˘ N(0; ), where R is a given noise-free rotation (the mean) and is a s”