Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
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DOI：
10.1109/icra46639.2022.9811613
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Peng Gao;Brian Reily;Rui Guo;Hongsheng Lu;Qingzhao Zhu;Hao Zhang
中科院分区：
文献类型：
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作者：
Peng Gao;Brian Reily;Rui Guo;Hongsheng Lu;Qingzhao Zhu;Hao Zhang
协作定位是一组机器人（如联网车辆）从多个角度协作估计对象位置的基本能力。为了实现协作定位，必须解决四个关键挑战，包括对观察对象之间的复杂关系建模，融合来自任意数量的协作机器人的观察，量化定位不确定性，以及解决机器人通信的延迟。在本文中，我们介绍了一种新的方法，集成了不确定性感知时空图学习和基于模型的状态估计的机器人团队协作定位对象。具体来说，我们引入了一个新的不确定性感知图学习模型，学习时空图来表示每个机器人随时间观察到的对象的历史运动，并提供对象定位的不确定性。此外，我们提出了一种新的方法，集成学习和基于模型的状态估计，融合异步观测从任意数量的机器人协作定位。我们评估我们的方法在两个协作对象定位的情况下，在模拟和真实的机器人。实验结果表明，我们的方法优于以往的方法，并实现了最先进的性能异步协作定位。
Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.