Maps generated by many visual Simultaneous Localization and Mapping algorithms consist of geometric primitives such as points, lines or planes. These maps offer a topographic representation of the environment, but they contain no semantic information about the environments. Object classifiers leveraging advances in machine learning are highly accurate and reliable, capable of detecting and classifying thousands of objects. Classifiers can be incorporated into a SLAM pipeline to add semantic information to a scene. Frequently, this semantic information is conducted for each frame of the image, but semantic labeling is not persistent over time. In this work, we present a nonparametric statistical approach to perform matching/association of objects detected over consecutive image frames. These associated classified objects are then localized in the accrued map using an unsupervised clustering method. We test our approach on multiple data sets, and it shows strong performance in terms of objects correctly associated from frame to frame. We also have tested our algorithm on three data sets in our lab environment using tag markers to demonstrate the accuracy of classified object localization process.

由许多可视化同时定位和制图算法生成的地图由诸如点，线或平面的几何图元组成。这些地图提供了环境的地形表示，但是它们不包含有关环境的语义信息。利用机器学习的先进性的对象分类器是高度准确和可靠的，能够检测和分类数千个对象。可以将分类器合并到SLAM管道中，以向场景添加语义信息。通常，针对图像的每个帧执行此语义信息，但是语义标记不会随时间而持久。在这项工作中，我们提出了一种非参数统计方法来执行在连续图像帧上检测到的对象的匹配/关联。然后，使用无监督聚类方法将这些关联的分类对象定位在应计映射中。我们在多个数据集上测试了我们的方法，并且在正确地将对象与帧之间关联的情况下，它显示了强大的性能。我们还使用标签标记在实验室环境中的三个数据集上测试了我们的算法，以证明分类对象定位过程的准确性。