Mapping plays a crucial role in location and navigation within automatic systems. However, the presence of dynamic objects in 3D point cloud maps generated from scan sensors can introduce map distortion and long traces, thereby posing challenges for accurate mapping and navigation. To address this issue, we propose ERASOR++, an enhanced approach based on the Egocentric Ratio of Pseudo Occupancy for effective dynamic object removal. To begin, we introduce the Height Coding Descriptor, which combines height difference and height layer information to encode the point cloud. Subsequently, we propose the Height Stack Test, Ground Layer Test, and Surrounding Point Test methods to precisely and efficiently identify the dynamic bins within point cloud bins, thus overcoming the limitations of prior approaches. Through extensive evaluation on open-source datasets, our approach demonstrates superior performance in terms of precision and efficiency compared to existing methods. Furthermore, the techniques described in our work hold promise for addressing various challenging tasks or aspects through subsequent migration.

翻译：地图构建在自动系统的定位与导航中起着关键作用。然而，由扫描传感器生成的三维点云地图中动态物体的存在会引起地图畸变和长轨迹，从而对精确建图和导航构成挑战。为解决这一问题，我们提出ERASOR++，一种基于伪占据以自我中心比率的增强方法，用于高效移除动态物体。首先，我们引入高度编码描述符，结合高度差与高度层信息对点云进行编码。随后，我们提出高度堆栈测试、地面层测试和周围点测试方法，以精确高效地识别点云体素中的动态体素，从而克服先前方法的局限性。通过在开源数据集上的广泛评估，与现有方法相比，我们的方法在精确度和效率方面均展现出更优性能。此外，本文所述技术有望通过后续迁移，应用于解决各类具有挑战性的任务或问题。