Boosting Self-localization with Graph Convolutional Neural Networks
Boosting Self-localization with Graph Convolutional Neural Networks
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DOI：
10.5220/0010212908610868
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Koji Takeda;Kanji Tanaka
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文献类型：
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作者：
Koji Takeda;Kanji Tanaka
场景图表示最近因其在视觉机器人自定位方面的灵活性和描述性而受到关注。在典型的自定位应用中，环境地图的对象、对象特征和对象关系分别作为节点、节点特征和边投影到场景图上，并随后使用图匹配引擎映射到查询场景图。然而，这种系统的计算、存储和通信开销成本与图节点的特征维度数量成正比，这在大规模应用中通常很重要。在这项研究中，我们展示了图卷积神经网络（GCN）与图匹配引擎一起训练和预测的可行性。然而，视觉特征通常不能很好地转化为现代图卷积模型中的图特征，从而影响其性能。因此，我们开发了一种新颖的知识转移框架，引入任意自定位模型作为教师来训练基于 GCN 的自定位系统，即学生。此外，该框架通过将教师模型的紧凑输出信号制定为训练数据，促进了轻量级存储和通信。 Oxford RobotCar 数据集的结果表明，所提出的方法优于现有的比较方法和教师自我定位系统。
Scene graph representation has recently merited attention for being flexible and descriptive where visual robot self-localization is concerned. In a typical self-localization application, the objects, object features and object relationships of the environment map are projected as nodes, node features and edges, respectively, on to the scene graph and subsequently mapped to a query scene graph using a graph matching engine. However, the computational, storage, and communication overhead costs of such a system are directly proportional to the number of feature dimensionalities of the graph nodes, often significant in large-scale applications. In this study, we demonstrate the feasibility of a graph convolutional neural network (GCN) to train and predict alongside a graph matching engine. However, visual features do not often translate well into graph features in modern graph convolution models, thereby affecting their performance. Therefore, we developed a novel knowledge transfer framework that introduces an arbitrary self-localization model as the teacher to train the GCN-based self-localization system i.e., the student. The framework, additionally, facilitated lightweight storage and communication by formulating the compact output signals from the teacher model as training data. Results on the Oxford RobotCar datasets reveal that the proposed method outperforms existing comparative methods and teacher self-localization systems.