EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
This work addresses the challenge of improving robustness and accuracy in semantic SLAM for applications like robotics and augmented reality, though it appears incremental as it builds on existing methods with specific enhancements.
The authors tackled the problem of robust object-level data association and pose estimation in semantic SLAM by proposing an ensemble data association strategy and an accurate pose estimation framework, resulting in a monocular SLAM system that significantly outperforms state-of-the-art techniques in accuracy and robustness on multiple datasets.
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on: https://github.com/yanmin-wu/EAO-SLAM.