Map merging of rotated, corrupted, and different scale maps using rectangular features

Abstract

Integrating data from multiple cooperative robots can be important for expanding their individual capabilities. In an environmental mapping scenario, multiple ground robots map different local areas. Algorithm complexity on merging the maps to build a global map depends on the three factors: orientation, accuracy and scale of the maps. When the three factors are all unknown, the map merging becomes a challenging problem. In this paper, a new approach on merging of two maps with the three factors are unknown. The idea is to estimate the best shared-areas by means of rectangular features. The information of dimensions and connections of maximal empty rectangles allows the algorithms to match orientations and scales, also to find overlapping points. The advantage of this approach is that a map merging is accomplished without any location estimations between the robots. This paper explains the map-merging process with an example of a simple environment, and presents a result with a practical environment.