In the field of indoor robotics, accurately navigating and mapping in dynamic environments using point clouds can be a challenging task due to the presence of dynamic points. These dynamic points are often represented by people in indoor environments, but in industrial settings with moving machinery, there can be various types of dynamic points. This study introduces DynaHull, a novel technique designed to enhance indoor mapping accuracy by effectively removing dynamic points from point clouds. DynaHull works by leveraging the observation that, over multiple scans, stationary points have a higher density compared to dynamic ones. Furthermore, DynaHull addresses mapping challenges related to unevenly distributed points by clustering the map into smaller sections. In each section, the density factor of each point is determined by dividing the number of neighbors by the volume these neighboring points occupy using a convex hull method. The algorithm removes the dynamic points using an adaptive threshold based on the point count of each cluster, thus reducing the false positives. The performance of DynaHull was compared to state-of-the-art techniques, such as ERASOR, Removert, OctoMap, and a baseline statistical outlier removal from Open3D, by comparing each method to the ground truth map created during a low activity period in which only a few dynamic points were present. The results indicated that DynaHull outperformed these techniques in various metrics, noticeably in the Earth Mover's Distance. This research contributes to indoor robotics by providing efficient methods for dynamic point removal, essential for accurate mapping and localization in dynamic environments.

翻译：在室内机器人领域，由于动态点的存在，使用点云在动态环境中精确导航和建图是一项具有挑战性的任务。这些动态点在室内环境中通常由行人代表，但在存在移动机械的工业场景中，动态点类型更为多样。本研究提出DynaHull——一种通过有效移除点云中动态点来提升室内建图精度的新型技术。DynaHull基于以下观察运行：在多帧扫描中，静态点的密度高于动态点。此外，DynaHull通过将地图聚类为更小的区块来解决点分布不均带来的建图挑战。在每个区块中，通过凸包法计算邻域点数量与这些点所占据体积的比值，从而确定每个点的密度因子。该算法基于每个簇的点数采用自适应阈值移除动态点，从而减少误检率。将DynaHull的性能与最新技术（如ERASOR、Removert、OctoMap以及Open3D中基于统计离群值移除的基线方法）进行了比较，通过将各方法与低活动期（仅存在少量动态点）构建的基准地图进行对比。结果表明，DynaHull在多项指标上均优于这些技术，尤其在地球移动距离方面表现突出。本研究为室内机器人领域提供了高效的动态点移除方法，这对动态环境中的精确建图与定位至关重要。