Unified Representation of Geometric Primitives for Graph-SLAM Optimization Using Decomposed Quadrics
This work addresses the need for compact and informative maps in SLAM systems, offering an incremental improvement over existing parameterizations.
The paper tackled the problem of parameterizing geometric primitives like points, lines, and planes for graph-SLAM by introducing a unified representation based on decomposed quadrics, resulting in better efficiency and robustness to noise in simulations and enabling compact maps in real-world experiments.
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.