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

Yanmin Wu, Yunzhou Zhang, Delong Zhu, Yonghui Feng, Sonya Coleman, Dermot Kerr

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

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
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 stateof-the-art techniques in accuracy and robustness. The source
code is available on https://github.com/yanmin-wu/
EAO-SLAM.
Original languageEnglish
Pages4966
Number of pages4973
Publication statusPublished - 28 Oct 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Las Vegas, United States
Duration: 25 Oct 202029 Oct 2020

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abbreviated titleIEEE IROS 2020
CountryUnited States
Period25/10/2029/10/20

Fingerprint Dive into the research topics of 'EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association'. Together they form a unique fingerprint.

Cite this