论文标题
DGORL：基于分布式图优化的多机器人系统的相对定位
DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems
论文作者
论文摘要
优化问题是许多机器人技术估计，计划和最佳控制问题的核心。基于模型的多机器人本地化已经进行了几次尝试，很少有人提出多机器人协作本地化问题，作为要通过图形优化解决的因素图问题。在这里，优化目标是最大程度地减少以分布式方式估计相对位置估计的错误。我们解决此问题的新型图理论方法由三个主要组成部分组成。 （连通性）图形形成，通过过渡模型扩展以及相对姿势的优化。首先，我们使用连接的机器人之间接收的信号强度估算相对姿势连接图，表明它们之间的相对范围。然后，我们使用运动模型来制定图形扩展并使用G $^2 $ O图优化作为动态网络的分布式求解器进行优化。最后，我们从理论上分析算法，并通过广泛的模拟来验证其最优性和性能。结果表明，与多机器人系统中协作定位的最新算法相比，该解决方案的实用性。
An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g$^2$o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.
