Simultaneous Localization and Mapping for Exploration with Stochastic Cloning EKF

摘要

While exploring unknown territory on search and rescue missions, fusing multiple sensors is vital for the precise on-line localization of mobile robots. The Extended Kalman filter (EKF) with stochastic cloning is well suited for this purpose and allows to directly integrate multiple absolute and relative state measurements. The latter measure differences between a past state and the current state, thus introducing correlations. These inter-dependencies are modeled by stochastic cloning, which performs a state augmentation by cloning the respective state estimates connected by a relative measurement. Different approaches to feed back information from absolute updates to the cloned state are presented and compared to a SLAM algorithm based on factor graph optimization.