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In most SLAM (Simultaneous Localization and Mapping) ap proaches, there is only unilateral data stream from data association (DA) to state estimation (SE), and the SE model estimates states according to the results of DA. This paper focuses on the reciprocity between DA and SE, and an incremental algorithm with inter-calibration between SE and DA is presented. Our approach uses a tree model called correspondence tree (CT) to represent the solution space of data association. CT is layered according to time steps and every node in it is a data association hypothesis for all the measurements gotten at the same time step. A best-first search with limited back-tracking is designed to find the optimal path in CT, and a state estimation approach based on the least-squares method is used to compute the cost of nodes in CT and update state estimation incrementally, so direct feedback is introduced from the SE to DA. With the interaction between DA and SE, and combining with tree pruning techniques, our approach can get accurate data association and state estimation for on-line SLAM applications.

An Incremental SLAM Algorithm with Inter-calibration between State Estimation and Data Association