Abstract
In this paper, we will demonstrate how Manhattan structure can be exploited to transform the Simultaneous Localization and Mapping (SLAM) problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher order layout structures, namely walls, floors, and ceilings. Furthermore, we show how our novel formulation leads to an optimization procedure that automatically performs data association and loop closure and which ultimately produces the simplest model of the environment that is consistent with the available measurements. We verify our method on real world data sets collected with various sensing modalities.
Abstract (translated)
在本文中，我们将演示如何利用曼哈顿结构将同时定位和映射（SLAM）问题转换为通过高阶凸优化求解的模型选择问题，该问题通常通过对特征位置的非线性优化来解决。布局结构，即墙壁，地板和天花板。此外，我们展示了我们的新颖配方如何导致自动执行数据关联和循环闭合的优化过程，并最终生成与可用测量一致的最简单环境模型。我们在使用各种传感模式收集的真实世界数据集上验证了我们的方法。
URL
https://arxiv.org/abs/1809.04135
