Object SLAM-Based Active Mapping and Robotic Grasping

Yanmin Wu, Yunzhou Zhang, Delong Zhu, Xin Chen, Sonya Coleman, Wenkai Sun, Xinggang Hu, Zhiqiang Deng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


This paper presents the first active object mapping framework for complex robotic manipulation and autonomous perception tasks. The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping. Aiming to reduce the observation uncertainty on target objects and increase their pose estimation accuracy,we also design an object-driven exploration strategy to guide the object mapping process,enabling autonomous mapping and high-level perception. Combining the mapping module and the exploration strategy,an accurate object map that is compatible with robotic grasping can be generated. Additionally,quantitative evaluations also indicate that the proposed framework has a very high mapping accuracy. Experiments with manipulation (including object grasping and placement) and augmented reality significantly demonstrate the effectiveness and advantages of our proposed framework.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665426886
Publication statusPublished online - 6 Jan 2022
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: 1 Dec 20213 Dec 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021


Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021 IEEE.


  • active mapping
  • augmented reality
  • Object SLAM
  • Robotic Grasping


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