{"title":"Uncertainty-aware correspondence identification for collaborative perception","authors":"Peng Gao, Qingzhao Zhu, Hao Zhang","doi":"10.1007/s10514-023-10086-9","DOIUrl":null,"url":null,"abstract":"<div><p>Correspondence identification is essential for multi-robot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our new approach formulates correspondence identification as a deep graph matching problem, which identifies correspondences based on deep graph neural network-based features and explicitly quantify uncertainties in the identified correspondences under the Bayesian framework. In addition, we design a novel loss function that explicitly reduces correspondence uncertainty and perceptual non-covisibility during learning. Finally, we design a novel multi-robot sensor fusion method that integrates the multi-robot observations given the identified correspondences to perform collaborative object localization. We evaluate our approach in the robotics applications of collaborative assembly, multi-robot coordination and connected autonomous driving using high-fidelity simulations and physical robots. Experiments have shown that, our approach achieves the state-of-the-art performance of correspondence identification. Furthermore, the identified correspondences of objects can be well integrated into multi-robot collaboration for object localization.\n</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Robots","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10514-023-10086-9","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1
Abstract
Correspondence identification is essential for multi-robot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our new approach formulates correspondence identification as a deep graph matching problem, which identifies correspondences based on deep graph neural network-based features and explicitly quantify uncertainties in the identified correspondences under the Bayesian framework. In addition, we design a novel loss function that explicitly reduces correspondence uncertainty and perceptual non-covisibility during learning. Finally, we design a novel multi-robot sensor fusion method that integrates the multi-robot observations given the identified correspondences to perform collaborative object localization. We evaluate our approach in the robotics applications of collaborative assembly, multi-robot coordination and connected autonomous driving using high-fidelity simulations and physical robots. Experiments have shown that, our approach achieves the state-of-the-art performance of correspondence identification. Furthermore, the identified correspondences of objects can be well integrated into multi-robot collaboration for object localization.
期刊介绍:
Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development.
The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.