Object SLAM-Based Active Mapping and Robotic Grasping


Yanmin Wu, 张 云洲, Delong Zhu, Xin Chen, Sonya Coleman, Wenkai Sun, Xinggang Hu and Zhiqiang Deng


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. 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. By 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 show that the proposed framework has a very high mapping accuracy. Manipulation experiments, including object grasping, object placement, and the augmented reality, significantly demonstrate the effectiveness and advantages of our proposed framework.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021