Safe navigation with simultaneous localization and mapping (SLAM) for autonomous robots is crucial in challenging environments. To achieve this goal, detecting moving objects in the surroundings and building a static map are essential. However, existing moving object segmentation methods have been developed separately for each field, making it challenging to perform real-time navigation and precise static map building simultaneously. In this paper, we propose an integrated real-time framework that combines online tracking-based moving object segmentation with static map building. For safe navigation, we introduce a computationally efficient hierarchical association cost matrix to enable real-time moving object segmentation. In the context of precise static mapping, we present a voting-based method, DS-Voting, designed to achieve accurate dynamic object removal and static object recovery by emphasizing their spatio-temporal differences. We evaluate our proposed method quantitatively and qualitatively in the SemanticKITTI dataset and real-world challenging environments. The results demonstrate that dynamic objects can be clearly distinguished and incorporated into static map construction, even in stairs, steep hills, and dense vegetation.

翻译：在复杂环境中，基于同步定位与建图（SLAM）的自主机器人安全导航至关重要。为实现这一目标，检测周围环境中的运动物体并构建静态地图是必不可少的。然而，现有的运动物体分割方法通常针对不同领域独立开发，难以同时实现实时导航与精确静态地图构建。本文提出一种集成式实时框架，将基于在线跟踪的运动物体分割与静态地图构建相结合。针对安全导航需求，我们引入一种计算高效的分层关联代价矩阵，以实现实时运动物体分割。在精确静态建图方面，我们提出一种基于投票的方法——DS-Voting，该方法通过强调动态与静态物体的时空差异，实现准确的动态物体移除与静态物体恢复。我们在SemanticKITTI数据集及真实世界复杂环境中对所提方法进行了定量与定性评估。结果表明，即使在楼梯、陡坡及茂密植被等场景中，动态物体也能被清晰区分并有效融入静态地图构建过程。