When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environment arises naturally. However, so far, the use of a particular structure prior has been coupled to working with a particular representation. We describe a system that supports inference with multiple priors while keeping the same dense representation. The priors are rigorously described by the user in a domain-specific language. Even though we work very close to the measurement space, we are able to represent structure constraints with the same expressivity as methods based on geometric primitives. This approach allows the intrinsic degrees of freedom of the environment's shape to be recovered. Experiments with simulated and real data sets will be presented. © 2012 Springer-Verlag.
CITATION STYLE
De La Puente, P., & Censi, A. (2012). Dense map inference with user-defined priors: From priorlets to scan eigenvariations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7463 LNAI, pp. 94–113). https://doi.org/10.1007/978-3-642-32732-2_6
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