DHDP-SLAM: Dynamic Hierarchical Dirichlet Process based data association for semantic SLAM

Abstract

The key to a semantic SLAM system lies in the data association between measurements and landmarks, using the association results to provide constraints for the pose estimation of robot. However, to address the issues in data association models where data continuity and similarity are not sufficiently emphasized and single-level association strategies exhibit low robustness, we propose a data association method based on the Dynamic Hierarchical Dirichlet Process (DHDP), which is an online data association model that can make full use of the continuity and similarity between data to improve the convergence speed of the model, and at the same time, it can also dynamically take into account the influence of previous data on the current data. Additionally, DHDP has a more robust two-level association strategy to improve the accuracy of data association. In the experiments, three different datasets (Simulation dataset, KITTI dataset and TUM dataset) were selected to validate the proposed method, and the results show that DHDP has faster convergence speed and higher association accuracy, and it is able to provide additional constraints to the system when integrating it into the SLAM system, and by compared it with the state-of-the-art SLAM methods, the DHDP-SLAM exhibits higher localization accuracy.