We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

中文翻译：

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借助地图改进了可扩展的在线学习空间概念和语言模型

我们提出了一种新颖的在线学习算法，称为SpCoSLAM 2.0，用于具有高精度和可伸缩性的空间概念和词汇获取。以前，我们提出SpCoSLAM作为基于无监督贝叶斯概率模型的在线学习算法，该模型集成了多模式场所分类，词法获取和SLAM。然而，由于学习的早期阶段的影响，我们的原始算法的估计准确性有限，并且在增加训练数据的情况下增加了计算复杂度。因此，我们引入诸如固定滞后回春之类的技术，以减少计算时间，同时保持比原始算法更高的精度。结果表明，在估计精度方面，该算法超越了原有算法，可与批量学习相提并论。此外，所提出算法的计算时间不依赖于训练数据的数量，并且对于可伸缩算法的每个步骤都变得恒定。我们的方法将有助于实现人与机器人之间的长期空间语言交互。