Heterogeneous Map Merging: State of the Art

Multi-robot mapping and environment modeling have several advantages that make<br />it an attractive alternative to the mapping with a single robot: faster exploration, higher<br />fault tolerance, richer data due to different sensors being used by different systems. However,<br />the environment modeling with several robotic systems operating in the same area causes problems<br />of higher-order&#8212;acquired knowledge fusion and synchronization over time, revealing the same<br />environment properties using different sensors with different technical specifications. While the<br />existing robot map and environment model merging techniques allow merging certain homogeneous<br />maps, the possibility to use sensors of different physical nature and different mapping algorithms is<br />limited. The resulting maps from robots with different specifications are heterogeneous, and even<br />though some research on how to merge fundamentally different maps exists, it is limited to specific<br />applications. This research reviews the state of the art in homogeneous and heterogeneous map<br />merging and illustrates the main research challenges in the area. Six factors are identified that<br />influence the outcome of map merging: (1) robotic platform hardware configurations, (2) map<br />representation types, (3) mapping algorithms, (4) shared information between robots, (5) relative<br />positioning information, (6) resulting global maps.

Abstract licence: CC BY

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