Decentralized Cooperative Multi-Robot Localization with EKF
This work addresses multi-robot localization for cooperative tasks in robotics, presenting an incremental improvement with a decentralized scheme.
The paper tackles the problem of localizing multiple robots in large featureless environments by proposing a decentralized approach that requires only one stationary robot as a temporary landmark. The result is validated through simulations and experiments with five robots, showing reduced storage and computational costs, and improved robustness against insufficient observations.
Multi-robot localization has been a critical problem for robots performing complex tasks cooperatively. In this paper, we propose a decentralized approach to localize a group of robots in a large featureless environment. The proposed approach only requires that at least one robot remains stationary as a temporary landmark during a certain period of time. The novelty of our approach is threefold: (1) developing a decentralized scheme that each robot calculates their own state and only stores the latest one to reduce storage and computational cost, (2) developing an efficient localization algorithm through the extended Kalman filter (EKF) that only uses observations of relative pose to estimate the robot positions, (3) developing a scheme has less requirements on landmarks and more robustness against insufficient observations. Various simulations and experiments using five robots equipped with relative pose-measurement sensors are performed to validate the superior performance of our approach.