论文标题
得分：范围辅助大满贯的二阶圆锥初始化
SCORE: A Second-Order Conic Initialization for Range-Aided SLAM
论文作者
论文摘要
我们提出了一种新颖的初始化技术，用于同时介绍的同时定位和映射（RA-SLAM）问题。在RA-SLAM中，除了测量对地标或姿势变量的刚性转换外，我们还考虑了点对点距离的测量。 RA-SLAM的标准配方将问题视为非凸优化，这需要良好的初始化才能获得质量结果。此处提出的初始化技术将RA-SLAM问题放松到凸问题上，然后解决该问题以确定原始非凸问题的初始化。放松是一个二阶锥体程序（SOCP），它源自RA-SLAM问题的四二次二次二次程序（QCQP）。作为SOCP，该方法是高度可扩展的。我们为RA-SLAM（得分）命名了这种放松的二阶圆锥放松。据我们所知，这项工作代表了Ra-Slam的第一个放松。我们提出了现实世界和模拟实验，这些实验显示得分初始化允许有效恢复质量解决方案，以使用成千上万的姿势和范围测量值来进行各种具有挑战性的单机器人和多机器人RA-SLAM问题。
We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.
