Incremental SLAM with backtracking data association for mobile robots

This paper focuses on the reciprocity between data association and state estimation in the simultaneous localization and mapping (SLAM) problem for mobile robot, and an incremental SLAM algorithm with backtracking data association is presented. Our approach uses a tree model called correspondence tree (CT) to represent the solution space of the data association problem. CT is layered according to time steps and every node in it is a data association hypothesis for the measurements gotten at a time. A best-first with limit backtracking search strategy is designed to find the optimal path in CT. A state estimation method based on the least-squares problem is developed. This method can compute the cost of nodes in CT and update state estimation incrementally, so direct feedback is introduced from the state estimation process to the data association model. With the interaction between data association and state estimation, and combining with tree pruning techniques, our approach can get accurate data association and state estimation for on-line SLAM applications.