Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
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DOI：
10.1109/icra48506.2021.9561810
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Peng Gao;Rui Guo;Hongsheng Lu;Hao Zhang-
中科院分区：
文献类型：
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作者：
Peng Gao;Rui Guo;Hongsheng Lu;Hao Zhang-
协同目标定位的目的是协同地估计从多个视图或视角观察到的目标的位置，这是多智能体系统（如联网车辆）的关键能力。为了实现协作定位，已经开发了几种基于模型的状态估计和基于学习的定位方法。鉴于其令人鼓舞的性能，基于模型的状态估计通常缺乏对多个对象之间的复杂关系进行建模的能力，而基于学习的方法通常无法融合来自任意数量的视图的观测，并且无法很好地对不确定性进行建模。在本文中，我们介绍了一种新的时空图滤波器的方法，集成了图学习和基于模型的估计进行多视图传感器融合的协作对象定位。我们的方法模型复杂的对象关系，使用一个新的时空图表示和融合多视图观察贝叶斯的方式，以提高不确定性下的位置估计。我们评估我们的方法在连接自动驾驶和多个行人定位的应用。实验结果表明，我们的方法优于以往的技术，并实现了国家的最先进的协同定位性能。
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaborative localization.