  In Simultaneous Localization And Mapping (SLAM) problems, high-level
landmarks have the potential to build compact and informative maps compared to
traditional point-based landmarks. In this work, we focus on the
parameterization of frequently used geometric primitives including points,
lines, planes, ellipsoids, cylinders, and cones. We first present a unified
representation based on quadrics, leading to a consistent and concise
formulation. Then we further study a decomposed model of quadrics that
discloses the symmetric and degenerated properties of a primitive. Based on the
decomposition, we develop geometrically meaningful quadrics factors in the
settings of a graph-SLAM problem. Then in simulation experiments, it is shown
that the decomposed formulation has better efficiency and robustness to
observation noises than baseline parameterizations. Finally, in real-world
experiments, the proposed back-end framework is demonstrated to be capable of
building compact and regularized maps.
