In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals. Special emphasis will be on range-based localization. Range and trajectory smoothness constraints are constructed in a position graph, then the robot trajectory over a sliding window is estimated by a graph based optimization algorithm. Moreover, convergence analysis of the algorithm is provided, and the effects of the number of iterations and window size in the optimization on the localization accuracy are analyzed. Extensive experiments on quadcopter under a variety of scenarios verify the effectiveness of the proposed algorithm and demonstrate a much higher localization accuracy than the existing range-based localization methods, especially in the altitude direction.

中文翻译：

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基于范围定位的图优化方法

在本文中，我们提出了一种基于通用图优化的定位框架，它可以适应具有不同测量时间间隔的不同类型的测量。将特别强调基于范围的本地化。在位置图中构建范围和轨迹平滑约束，然后通过基于图的优化算法估计滑动窗口上的机器人轨迹。并给出了算法的收敛性分析，分析了优化中迭代次数和窗口大小对定位精度的影响。四轴飞行器在各种场景下的大量实验验证了所提出算法的有效性，并证明了比现有基于距离的定位方法更高的定位精度，