SLAM and moving target tracking based on constrained local submap filter

The issues of simultaneous localization and mapping (SLAM) and moving target tracking are investigated in this paper. In EKF-based SLAM, the computational complexity significantly increases with the growing number of landmarks to be estimated. To solve this problem, the constrained local submap filter (CLSF) is used in this paper to reduce the computational complexity of SLAM and target tracking. An approach to localizing the robot, building the map of environment and tracking moving target simultaneously based on CLSF is proposed. The pose of the robot, the position of the landmarks and the states of the target are integrated into a system state vector defined in CLSF framework and estimated by EKF. Finally, simulation results demonstrate the effectiveness of the proposed approach.

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