  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.
