A right invariant extended Kalman filter for object based SLAM

Yang Song*, Zhuqing Zhang, Jun Wu, Yue Wang, Liang Zhao, Shoudong Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLAM process. In this letter, based on a novel Lie group structure, a right invariant extended Kalman filter (RI-EKF) for object based SLAM is proposed. The observability analysis shows that the proposed algorithm automatically maintains the correct unobservable subspace, while standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better consistency for the proposed algorithm comparing to Std-EKF. Finally, simulations and real world experiments validate not only the consistency and accuracy of the proposed algorithm, but also the practicability of the proposed RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made publicly available.
Original languageEnglish
Pages (from-to)1316-1323
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
Publication statusPublished - 31 Dec 2021

Keywords / Materials (for Non-textual outputs)

  • localization
  • mapping
  • SLAM

Fingerprint

Dive into the research topics of 'A right invariant extended Kalman filter for object based SLAM'. Together they form a unique fingerprint.

Cite this