Multi-robot mapping and environment modeling have several advantages that make it an attractive alternative to the mapping with a single robot: faster exploration, higher fault tolerance, richer data due to different sensors being used by different systems. However, the environment modeling with several robotic systems operating in the same area causes problems of higher-order—acquired knowledge fusion and synchronization over time, revealing the same environment properties using different sensors with different technical specifications. While the existing robot map and environment model merging techniques allow merging certain homogeneous maps, the possibility to use sensors of different physical nature and different mapping algorithms is limited. The resulting maps from robots with different specifications are heterogeneous, and even though some research on how to merge fundamentally different maps exists, it is limited to specific applications. This research reviews the state of the art in homogeneous and heterogeneous map merging and illustrates the main research challenges in the area. Six factors are identified that influence the outcome of map merging: (1) robotic platform hardware configurations, (2) map representation types, (3) mapping algorithms, (4) shared information between robots, (5) relative positioning information, (6) resulting global maps.
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