Relative observation for multi-robot collaborative localisation based on multi-source signals

This paper describes a synthesizing method for multi-robot collaborative localization. A distributed extended Kalman filter based on robot odometry and external North Star signals for data fusion is first designed for the localization of individuals in the robot group. Relying on relative observation by infrared sensors and gyroscopes mounted on robots, and the ‘uncertainty volume’ strategy, the positions estimated by extended Kalman filters are further corrected for precising the localization process. The localization accuracy based on different sensing regimes is tested. Sensor correlations and uncertainties are analyzed for predicting error propagation and to accommodate sensing deviations. The multi-source signals are then synthesized for the collaborative localization for a multi-robot system without introducing excessive computation. Finally, this work is verified by both simulation and experiments with real robots, i.e. the Festo Robotinos under different scenarios.