Kalman filter based cooperative landmark localization in indoor environment for mobile robots

Cost-effective localization in indoor environments is a critical task in numerous applications of mobile robots. Sensors on board of individual robots may have low accuracy, but the fact that they share the same environment may be exploited to design a cooperative localization technique. Our approach assumes that the mobile robots can share current and past measurements of their on-board sensors with each other thanks to some communication scheme and to a database of such measurements. The presented method is based on the Bayes theorem. By fusing the measurements of multiple agents, a higher accuracy can be achieved than any of the separate units might be able to reach considering the landmark locations. Simulation results in a testbed motion scenario show the advantages of the method presented.

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