Object SLAM-Based Active Mapping and Robotic Grasping

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...

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Bibliographic Details
Published in2021 International Conference on 3D Vision (3DV) pp. 1372 - 1381
Main Authors Wu, Yanmin, Zhang, Yunzhou, Zhu, Delong, Chen, Xin, Coleman, Sonya, Sun, Wenkai, Hu, Xinggang, Deng, Zhiqiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2021
Subjects
active mapping
augmented reality
Grasping
Object SLAM
Pose estimation
Robotic Grasping
Shape
Simultaneous localization and mapping
Solid modeling
Three-dimensional displays
Uncertainty
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Summary: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.
ISSN:2475-7888
DOI:10.1109/3DV53792.2021.00144