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Long-term object search using incremental scene graph updating

Published online by Cambridge University Press:  22 August 2022

Fangbo Zhou Open the ORCID record for Fangbo Zhou [Opens in a new window] ,
Huaping Liu ,
Huailin Zhao  and
Lanjun Liang
Fangbo Zhou
Affiliation:
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China
Huaping Liu*
Affiliation:
Department of Computer Science and Technology, Tsinghua University, Beijing, China.
Huailin Zhao
Affiliation:
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China
Lanjun Liang
Affiliation:
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China
*
*Corresponding author. E-mail: hpliu@tsinghua.edu.cn.
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Abstract

Effective searching for target objects in indoor scenes is essential for household robots to perform daily tasks. With the establishment of a precise map, the robot can navigate to a fixed static target. However, it is difficult for mobile robots to find movable objects like cups. To address this problem, we establish an object search framework that combines navigation map, semantic map, and scene graph. The robot updates the scene graph to achieve a long-term target search. Considering the different start positions of the robots, we weigh the distance the robot walks and the probability of finding objects to achieve global path planning. The robot can continuously update the scene graph in a dynamic environment to memorize the position relation of objects in the scene. This method has been realized in both simulation and real-world environments. The experimental results show the feasibility and effectiveness of this method.

Keywords

household robotlong-term object searchincremental scene graph

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 3 , March 2023 , pp. 962 - 975
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

†

This work was completed while Fangbo Zhou was visiting Tsinghua University, Beijing, China.

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