Document Type
Article
Keywords
cognitive robotics, navigation, sensory fusion
Disciplines
Computer Engineering | Robotics
Abstract
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 .
Article Number
1011
Publication Date
4-2013
Recommended Citation
Lyons, Damian M.; Shrestha, Karma; and Liu, Tsung-Ming, "Fusion of Ranging Data From Robot Teams Operating in Confined Areas" (2013). Faculty Publications. 12.
https://research.library.fordham.edu/frcv_facultypubs/12
Comments
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, SPIE Defense Security and Sensing Symposium, Baltimore MD, April 2013
This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see http://www.cis.fordham.edu/graduate.html, and the Fordham University Graduate School of Arts and Sciences, see http://www.fordham.edu/gsas.