Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new location that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the sytems's output. As novelties, our work includes a multi-scale match matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state-of-the-art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive ACML solution, obtaining good accuracy and recovery times.

翻译：自定位是移动机器人导航系统集成的基本能力,以便使用地图从一个点移动到另一个点。 因此, 本地化精确度的任何提高对于执行微妙的灵活任务至关重要。 本文描述了一个使用蒙特卡洛本地化算法( MCL) 维持数组粒子的新位置, 总是选择最佳的算法作为音节的输出。 作为新颖之处, 我们的工作包括一个多级匹配算法, 以创造新的 MCL 群, 以及确定最可靠的度量度。 它还有助于最先进的实施, 提高错误估计或未知初始位置的恢复时间。 所提议方法在与 Nav2 完全整合的模块中在ROS2 中进行了评估, 并与当前最先进的适应性ACML 解决方案进行比较, 获得良好的准确性和恢复时间 。