Skip to Main Content
Localization of nodes within a sensing network is a fundamental requirement for many applications. This paper proposes a method by which sensors self-localize based on their uncertain observations of other nodes in the network, using both Monte Carlo and Kalman filtering techniques. The proposed methods are demonstrated in a laboratory environment where stationary camera nodes self-localized in real-time by observing Pioneer robots moving about within their field of view. The robots take observations of surveyed beacons in the environment and provide estimates of their poses to the rest of the network.