SUMMARYThis article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

中文翻译：

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循环闭合检测中数据降维方法的开发：一种增量方法

摘要本文提出了一种用于机器人自主导航的闭环检测中增量数据降维的方法。该方法将主要特征向量概念用于：（a）视觉数据集的光谱描述和（b）低维表示。与大多数其他关于数据降维的论文（以批处理模式完成）不同，我们的方法结合了滑动窗口技术和坐标变换来实现增量数据的降维。在模拟和真实场景中进行了实验，结果是合适的。