  Inferring the posterior distribution in SLAM is critical for evaluating the
uncertainty in localization and mapping, as well as supporting subsequent
planning tasks aiming to reduce uncertainty for safe navigation. However,
real-time full posterior inference techniques, such as Gaussian approximation
and particle filters, either lack expressiveness for representing non-Gaussian
posteriors or suffer from performance degeneracy when estimating
high-dimensional posteriors. Inspired by the complementary strengths of
Gaussian approximation and particle filters$\unicode{x2013}$scalability and
non-Gaussian estimation, respectively$\unicode{x2013}$we blend these two
approaches to infer marginal posteriors in SLAM. Specifically, Gaussian
approximation provides robot pose distributions on which particle filters are
conditioned to sample landmark marginals. In return, the maximum a posteriori
point among these samples can be used to reset linearization points in the
nonlinear optimization solver of the Gaussian approximation, facilitating the
pursuit of global optima. We demonstrate the scalability, generalizability, and
accuracy of our algorithm for real-time full posterior inference on realworld
range-only SLAM and object-based bearing-only SLAM datasets.
