Optimized Data Association Based on Gaussian Mixture Model

Abstract

Data association is the foundation of state estimation in mobile robot simultaneous localization and mapping. Aiming at the problems of false association, high computational complexity in joint compatible branch and bound algorithm, we propose an optimized joint compatible branch and bound data association algorithm based on Gaussian mixture clustering. Firstly, the local association strategy is adopted to limit data association in local region, so as to reduce the number of features involved in data association at the current moment. Secondly, the Gaussian mixture clustering algorithm is used in local areas to group the observed values at the current moment, so as to get several groups that have little correlation with each other. Finally, joint compatible branch and bound data association algorithm is used in each group for data association, and the optimal solution is obtained according to mutual exclusion criteria and optimal criteria. The experiment results verify that the algorithm improved the accuracy of data association, reduced the computational complexity and improved the efficiency of data association.

Keywords

• Artificial intelligence
• autonomous agents
• clustering algorithms
• intelligent robots
• simultaneous localization and mapping