We address the problem of fusing laser ranging data from multiple mobile robots that are surveying an area as part of a robot
search and rescue or area surveillance mission. We are specifically interested in the case where members of the robot team are
working in close proximity to each other. The advantage of this teamwork is that it greatly speeds up the surveying process; the area
can be quickly covered even when the robots use a random motion exploration approach. However, the disadvantage of the close
proximity is that it is possible, and even likely, that the laser ranging data from one robot include many depth readings caused by
another robot. We refer to this as mutual interference.
Using a team of two Pioneer 3-AT robots with tilted SICK LMS-200 laser sensors, we evaluate several techniques for fusing
the laser ranging information so as to eliminate the mutual interference. There is an extensive literature on the mapping and
localization aspect of this problem. Recent work on mapping has begun to address dynamic or transient objects. Our problem differs
from the dynamic map problem in that we look at one kind of transient map feature, other robots, and we know that we wish to
completely eliminate the feature.
We present and evaluate three different approaches to the map fusion problem: a robot-centric approach, based on
estimating team member locations; a map-centric approach, based on inspecting local regions of the map, and a combination of both
approaches. We show results for these approaches for several experiments for a two robot team operating in a confined indoor