LDSO：具有回环检测的直接稀疏里程计
使用无参数统计和聚类实现SLAM中识别物体的定位：Localization of Classified Objects in SLAM using Nonparametric Statistics and Clustering
Abstract—Traditional Simultaneous Localization and Mapping (SLAM) approaches build maps based on points, lines or planes. These maps visually resemble the environment but without any semantic or information about the objects in the environment. Recent advancements in machine learning have made object detection highly accurate and reliable with large set of objects. Object detection can effectively help SLAM to incorporate semantics in the mapping process. One of the main obstacles is data association between detected objects over time. We demonstrate a nonparametric statistical approach to solve the data association between detected objects over consecutive frames. Then we use an unsupervised clustering method to identify the existence of objects in the map. The complete process can be run in parallel with SLAM. The performance of our algorithm is demonstrated on several public datasets, which shows promising results in locating objects in SLAM.
简介——传统的同步定位与建图（SLAM）方法基于点、线和平面来建图。这些地图在视觉上接近于环境但是没有任何的关于环境中的物体的语义或者信息。最近的关于机器学习中的进步通过大数量的物体使得物体识别变得高度准确可信。物体识别可以有效地帮助SLAM在建图过程中将语义包含进来。其中主要障碍之一是随着时间的推移，检测到的对象之间的数据关联。我们展示了一种非参数统计方法来解决连续帧上检测到的物体之间的数据关联。然后，我们使用无监督聚类方法来识别地图中存在的对象。以上整个过程可以与SLAM并行运行。通过对多个公共数据集的分析，证明了该算法的有效性，实现了在SLAM中对目标的定位。