Multi-modal and Inertial sensor Solutions for Navigation-type Factor Graphs
Name
FourieD_PhDThesis.pdf
Description
PhD thesis submitted to MIT EECS 31 Aug 2017
Size
15.66 MB
Format
Adobe PDF
Checksum (MD5)
901aaca414547510de62531ecd733a74
Author(s)
Fourie, Dehann
Date Issued
August 31, 2017
Publisher
MIT
Citation
Fourie, D., 2017. Multi-modal and inertial sensor solutions for navigation-type factor graphs (Doctoral dissertation, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution).
Abstract
This thesis presents a sum-product inference algorithm for platform navigation called Multi-modal iSAM (incremental smoothing and mapping). Common Gaussian only 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 max-product 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.
Subjects
SLAM, robotics, navigation, mapping, localization, factor graph, Bayes tree, non-Gaussian, multi-modal, junction tree, inertial odometry, preintegration, IMU, radar, acoustic, Chapman-Kolmogorov transit integral
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