Title :
Gaussian mixture model based high dimensional SLAM utilizing sparse grid quadrature
Author :
Turnowicz, Matthew R. ; Yang Cheng
Author_Institution :
Dept. of Aerosp. Eng., Mississippi State Univ., Starkville, MS, USA
Abstract :
A high-dimensional Simultaneous Localization and Mapping (SLAM) algorithm is presented that replaces the particles in FastSLAM with individual Gaussians. In addition, the high-dimensional vehicle state is partitioned into linear and nonlinear parts and the nonlinear part is approximated by a mixture of Gaussians of which the means and covariances are propagated and updated using sparse grid quadrature. Preliminary simulation results of three-dimensional SLAM show that the Gaussian mixture approach is more accurate than the particle based approach.
Keywords :
Gaussian processes; SLAM (robots); covariance analysis; mixture models; particle filtering (numerical methods); Gaussian mixture model; covariances; high dimensional SLAM; high-dimensional vehicle state; nonlinear parts; simultaneous localization and mapping; sparse grid quadrature; Accuracy; Noise; Noise measurement; Proposals; Simultaneous localization and mapping; Uncertainty; Vehicles; Estimation; Kalman filtering; Uncertain systems;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6859199
