Data Association Method For Mobile Robots In SLAM

The simultaneous localization and mapping (SLAM) problem is one of the key problems for a mobile robot to be truly autonomous in an unknown environment. However, in an unknown environment, for lack of priori map information and location information the mobile robot locates by the perception of environmental characteristics and estimate and at the same time to create map. Data association is a important part of SLAM. In this paper, we research and discuss the data association in SLAM.In this paper, by analyzing data association tree model and Bayesian model, there is a method to resolve data association problem for SLAM which is called as an SLAM algorithm based on heuristic graph search with dynamic threshold. This method uses back-searching to revise past error data associations and uses dynamic threshold to reduce possible associations. The algorithm doesn't lower the accuracy of data association and doesn't take more time to revise the past error data associations on line.The paper has done a lot of researches on robot's model. At the end, A simulation platform is created using the models to do experiments. Using the platform, we do experiments using FastSLAM, HBS_SLAM and DHBS_SLAM to test algorithm's performance. And do some analysises on the experiment's results. The accuracy of data association and time of data association improve that the DHBS_SLAM is a efficient algorithm for SLAM.