A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Abstract

Map merging is a noteworthy phenomenon for cases such as search and rescue and disaster areas in whichthe duration is quite significant when gathering information about an environment. It is obvious that the total mappingtime decreases if the number of agents (robots) increases. However, the use of multiple agents leads to problems suchas task allocation schemes and the fusing of local maps. Examining the present methods, it is generally observed thatthe common features of local maps have been found and the global map is formed by obtaining related transformationbetween local maps. However, such implementations may be risky when local maps have symmetrical areas. Hence, anovel and semantic approach has been developed to solve this problem. The developed method counts on the reliabilitylevel of feature points. If relevant feature points are trusted, local maps are merged according to the best point orpoints. The simulation results from a robot operating system and a real-time experiment support the proposed method’sefficiency, and mapping can be performed even for environments that have symmetrical similar parts and the task timecan thus be reduced.