论文标题
动态大满贯：速度的需求
Dynamic SLAM: The Need For Speed
论文作者
论文摘要
在大多数同时定位和映射（SLAM）算法中，静态世界假设是标准的。自治系统向非结构化动态环境的部署增加正在推动识别移动对象并实时估算其速度的需求。大多数现有的基于SLAM的方法都依赖于3D对象的3D模型的数据库或施加重大运动约束。在本文中，我们提出了一种新的基于功能的，无模型的，具有对象感知动态的动态算法，该算法利用语义分割以允许在场景中估算刚性对象的运动，而无需估计对象姿势或对其3D模型有任何先验知识。该算法生成动态和静态结构的地图，并具有提取场景中刚性移动对象的速度的能力。它的性能在模拟，合成和现实世界数据集上证明。
The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.
