论文标题
用运动中的对象猛击后端：一个统一的框架和教程
SLAM Backends with Objects in Motion: A Unifying Framework and Tutorial
论文作者
论文摘要
经常部署同时本地化和映射（SLAM）算法以支持广泛的机器人应用程序，例如在未知环境中的自主导航以及虚拟现实中的场景映射。这些应用中的许多都要求自主代理在高度动态的场景中执行大满贯。为此，本教程将最近引入的基于优化的SLAM后端框架扩展到具有移动对象和功能的环境。使用此框架，我们考虑了动态大满贯最近进步的和解。此外，我们提出了动态EKF SLAM：一种基于我们的框架生成的基于过滤的，基于过滤的动态SLAM算法，并证明它在数学上与经典EKF SLAM算法直接扩展到动态环境设置。使用模拟数据的经验结果表明，动态EKF大满贯可以实现高效率的较高本地化和移动对象姿势估计精度以及高映射精度。
Simultaneous Localization and Mapping (SLAM) algorithms are frequently deployed to support a wide range of robotics applications, such as autonomous navigation in unknown environments, and scene mapping in virtual reality. Many of these applications require autonomous agents to perform SLAM in highly dynamic scenes. To this end, this tutorial extends a recently introduced, unifying optimization-based SLAM backend framework to environments with moving objects and features. Using this framework, we consider a rapprochement of recent advances in dynamic SLAM. Moreover, we present dynamic EKF SLAM: a novel, filtering-based dynamic SLAM algorithm generated from our framework, and prove that it is mathematically equivalent to a direct extension of the classical EKF SLAM algorithm to the dynamic environment setting. Empirical results with simulated data indicate that dynamic EKF SLAM can achieve high localization and mobile object pose estimation accuracy, as well as high map precision, with high efficiency.
