Building maps of indoor environments by merging line segments extracted from registered laser range scans
The recent trend of deploying mobile robots in large indoor environments calls for development of efficient map representation techniques. Compared to the more common occupancy grid representation, maps built with line segments are more compact and scale well with the environment size. In this paper, we propose an offline method for building maps of indoor environments using line segments extracted from registered laser range scans. At the core of this method lies a new formulation for identifying and then merging into one, all line segments that represent the same planar surface in the environment. Two successive steps of density-based clustering, applied on the extracted segments, enable us to delineate the segments that are in close proximity to each other and hence represent the same surface. The proposed method has accurately built maps of a wide variety of indoor environments, both real and simulated. Compared to two other similar methods, it has generally produced better maps. We also propose ways by which the goodness of the produced maps, in terms of how closely they resemble the ground truth, can be assessed.
line segment clustering ground truth laser range data line merging map building map assessment