Fourie
Dehann
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Multi-modal and inertial sensor solutions for navigation-type factor graphs
Fourie
Dehann
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Thesis
This thesis presents a sum-product inference algorithm for platform navigation
called Multi-modal iSAM (incremental smoothing and mapping). CommonGaussian-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 maxproduct
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 lowlikelihood 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; rawbeam-formed acoustic measurements; or conventional parametric
likelihoods, and others.