论文标题
僵化：机器人本地化和映射在具有大型动态刚性对象的环境中
RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects
论文作者
论文摘要
这项工作提出了一种新型的RGB-D SLAM方法，可以同时细分，跟踪和重建静态背景和大型动态刚性对象，这些物体可以遮挡相机视图的主要部分。以前的方法将场景的动态部分视为离群值，因此仅限于场景中的少量变化，或依靠场景中所有对象的先验信息来启用可靠的相机跟踪。在这里，我们建议将所有动态零件视为一个刚体的身体，并同时分段并跟踪静态和动态组件。因此，我们可以在动态对象引起大闭塞的环境中同时定位和重建静态背景和刚性动态组件。我们在具有大型动态阻塞的多个具有挑战性的场景上评估我们的方法。评估表明，我们的方法可以实现更好的运动分割，定位和映射，而无需先验了解动态对象的形状和外观。
This work presents a novel RGB-D SLAM approach to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view. Previous approaches treat dynamic parts of a scene as outliers and are thus limited to a small amount of changes in the scene, or rely on prior information for all objects in the scene to enable robust camera tracking. Here, we propose to treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components. We, therefore, enable simultaneous localisation and reconstruction of both the static background and rigid dynamic components in environments where dynamic objects cause large occlusion. We evaluate our approach on multiple challenging scenes with large dynamic occlusion. The evaluation demonstrates that our approach achieves better motion segmentation, localisation and mapping without requiring prior knowledge of the dynamic object's shape and appearance.
