Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements

Fusing multiple sensors is vital for the precise online localization of mobile robots. The Extended Kalman filter is well suited for this purpose and allows to directly integrate multiple absolute measurements in the filter. However, multiple relative state measurements cannot be fused directly into the filter because they measure differences between a past state and the current state, which introduces correlations. These interdependencies can be modeled by stochastic cloning, which introduces a state augmentation by cloning the respective state estimates connected by a relative measurement. This paper investigates the impact of multiple mixed relative and absolute updates on the estimated state. An approach to feed back information from absolute updates to the cloned state by explicit cloning is presented and compared to existing implicit methods.