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.
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 language | English |
|---|---|
| Pages | 4966 |
| Number of pages | 4973 |
| Publication status | Published (in print/issue) - 28 Oct 2020 |
| Event | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Las Vegas, United States Duration: 25 Oct 2020 → 29 Oct 2020 |
Conference
| Conference | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|---|---|
| Abbreviated title | IEEE IROS 2020 |
| Country/Territory | United States |
| Period | 25/10/20 → 29/10/20 |
