In this article, a fast and reliable map-merging algorithm is proposed to produce a global two dimensional map of an indoor environment in a multi-robot simultaneous localization and mapping (SLAM) process. In SLAM process, to find its way in this environment, a robot should be able to determine its position relative to a map formed from its observations. To solve this complex problem, simultan...

We illustrate our experience in developing and implementing algorithms for map merging, i.e., the problem of fusing two or more partial maps without common reference frames into one large global map. The partial maps may for example be acquired by multiple robots, or during several runs of a single robot from varying starting positions. Our work deals with low quality maps based on probabilisti...

When multiple robots cooperatively explore an environment, maps from individual robots must be merged to produce a single globally consistent map. This is a difficult problem when the robots do not have a common reference frame or global positioning. In this paper, we describe an algorithm for merging embedded topological maps. Topological maps provide a concise description of the navigability ...

Learning maps from sensor data has been addressed since more than two decades by Simultaneous Localization and Mapping (SLAM) systems. Modern state-of-the-art SLAM approaches exhibit excellent performances and are able to cope with environments having the scale of a city. Usually these methods are entailed for on-line operation, requiring the data to be acquired in a single run, which is not al...