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.
|Title of host publication||Proceedings - 2021 International Conference on 3D Vision, 3DV 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published online - 6 Jan 2022|
|Event||9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom|
Duration: 1 Dec 2021 → 3 Dec 2021
|Name||Proceedings - 2021 International Conference on 3D Vision, 3DV 2021|
|Conference||9th International Conference on 3D Vision, 3DV 2021|
|Period||1/12/21 → 3/12/21|
Bibliographical notePublisher Copyright:
© 2021 IEEE.
- active mapping
- augmented reality
- Object SLAM
- Robotic Grasping