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Robust Monte Carlo localization for mobile robots
TLDR
A more robust algorithm is developed called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation of Monte Carlo Localization algorithms, and is applied to mobile robots equipped with range finders. Expand
Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. Expand
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
TLDR
The theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem, are presented, and both simulation results and actual SLAM experiments are presented that underscore the potential of these methods as an alternative to EKF-based approaches. Expand
MCMC-based particle filtering for tracking a variable number of interacting targets
TLDR
A particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets, is described and a novel Markov chain Monte Carlo (MCMC) sampling step is replaced to obtain a more efficient MCMC-based multitarget filter. Expand
iSAM: Incremental Smoothing and Mapping
TLDR
iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering and provides efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. Expand
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
TLDR
The preintegrated inertial measurement unit model can be seamlessly integrated into a visual--inertial pipeline under the unifying framework of factor graphs and the application of incremental-smoothing algorithms and the use of a structureless model for visual measurements, which avoids optimizing over the 3-D points, further accelerating the computation. Expand
iSAM2: Incremental smoothing and mapping using the Bayes tree
TLDR
The Bayes tree is applied to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. Expand
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
TLDR
Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. Expand
Factor Graphs and GTSAM: A Hands-on Introduction
In this document I provide a hands-on introduction to both factor graphs and GTSAM. Factor graphs are graphical models (Koller and Friedman, 2009) that are well suited to modeling complex estimationExpand
Recognizing emotion in speech
TLDR
A new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour, is presented, which obtains classification performance that is close to human performance on the task. Expand
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