EAO-SLAM: Monocular Semi-Dense Object SLAM  Based on Ensemble Data Association

Authors

  Yanmin Wu, Yunzhou Zhang*, Delong Zhu, Yonghui Feng, Sonya Coleman and Dermot Kerr
  ✉ wuyanminmax@gmail.com, * zhangyunzhou@mail.neu.edu.cn

Abstract

  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 to integrate 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 the association process. We then present an accurate object pose estimation framework, in which an outlier-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of the estimated 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 stateof-the-art techniques in accuracy and robustness.

Video

  YouTube | bilibili

  

Experimental Results


wuyanminmax@gmail.com
2020.02.20