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Correspondence identification for collaborative multi-robot perception under uncertainty
Autonomous Robots ( IF 3.5 ) Pub Date : 2021-08-11 , DOI: 10.1007/s10514-021-10009-6
Peng Gao 1 , Hao Zhang 1 , Rui Guo 2 , Hongsheng Lu 2
Affiliation  

Correspondence identification is a critical capability for multi-robot collaborative perception, which allows a group of robots to consistently refer to the same objects in their own fields of view. Correspondence identification is challenging due to the existence of non-covisible objects that cannot be observed by all robots, and due to uncertainty in robot perception. In this paper, we introduce a novel principled approach that formulates correspondence identification as a graph matching problem under the mathematical framework of regularized constrained optimization. We develop a regularization term to explicitly address perception uncertainties by penalizing the object correspondences with a high uncertainty. We also introduce a second regularization term to explicitly address non-covisible objects by penalizing the correspondences built by the non-covisible objects. Our approach is evaluated in robotic simulations and real physical robots. Experimental results show that our method is able to address correspondence identification under uncertainty and non-covisibility, and achieves the state-of-the-art performance.



中文翻译:

不确定性下协同多机器人感知的对应识别

对应识别是多机器人协同感知的关键能力,它允许一组机器人在他们自己的视野中一致地引用相同的对象。由于存在所有机器人无法观察到的非共可见物体,以及机器人感知的不确定性,对应识别具有挑战性。在本文中,我们介绍了一种新的原理方法,该方法在正则化约束优化的数学框架下将对应识别表述为图匹配问题。我们开发了一个正则化项,通过惩罚具有高不确定性的对象对应关系来明确解决感知不确定性。我们还引入了第二个正则化项,通过惩罚非共可见对象建立的对应关系来明确解决非共可见对象。我们的方法在机器人模拟和真实的物理机器人中得到了评估。实验结果表明,我们的方法能够解决不确定性和非共可见性下的对应识别问题,并达到了最先进的性能。

更新日期:2021-08-11
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