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 environment .

We address the problem of fusing laser and RGB-Data from multiple robots operating in close proximity to one another. By having a team of robots working together, a large area can be scanned quickly, or a smaller area scanned in greater detail. However, a key aspect of this problem is the elimination of the spurious readings due to the robots operating in close proximity. While there is an extensive literature on the mapping and localization aspect of this problem, our problem differs from the dynamic map problem in that it involves at one kind of transient map feature, robots viewing other robots, and we know that we wish to completely eliminate all such mutual views. In prior work, we investigated the problem of fusing laser data from multiple robots in such a manner as to reject this spurious data from other robots. This work showed that a combination of local robot-based direction filtering and global map-based visibility filtering at a central map server removed 91% of the spurious data and resulted in a 98% quality improvement. In this paper we additionally consider the problem of fusing RGB-D data generated by a stereo-camera sensor. An approach based on a model of human visual attention is presented and compared with our prior work and with other related work. This approach is an order of magnitude faster than the prior work and yet rejects 73% of the spurious data producing a 55% quality improvement. Results are shown for this approach for two experiments with a two robot team operating in a confined indoor environment (4m x 4m).