Abstract
The visual system is a central component of mobile robot navigation, as it provides essential information about the surrounding environment for motion planning and coordination. However, navigation requires the robot to interpret sensory data through localization and mapping processes, such as Simultaneous Localization and Mapping (SLAM) frameworks. Mapping such environments with a single robot remains a significant challenge due to limited coverage and the substantial time required to map large areas. Multi-robot SLAM offers a promising alternative by distributing the exploration task across several robots, yet practical implementations still struggle with issues such as inconsistent map merging and the overall computational complexity of generating a unified global map. The objective of this study is to implement and evaluate a multi-robot SLAM system in which robots operate independently, and maps are merged on a centralized server in a real labyrinth setting to determine whether collaborative mapping can be practically achieved using mobile turtlebot robots. The system integrates frontier-driven exploration with a two-stage inter-robot loop closure detection process, enabling robots to identify and validate overlapping regions during mapping. Global pose graph optimization is performed on a centralized server that aggregates data from all robots, while pose graph sparsification based on effective resistances reduces the amount of data that must be exchanged. Experiments were first conducted in simulation and later in a physical labyrinth constructed for testing. The results demonstrate that two robots were able to autonomously explore distinct regions, detect inter-robot loop closures, generate sparsified pose graphs, and merge their individual maps into a coherent global representation. The study provides an end-to-end implementation and establishes a foundation for scaling multi-robot SLAM to more complex scenarios.