论文标题
视觉惯性多实体动态大满贯，对象级重新定位
Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation
论文作者
论文摘要
在本文中，我们提出了一个紧密耦合的视觉惯性对象级多实体动态大满贯系统。即使在极其动态的场景中，它也可以针对摄像头姿势，速度，IMU偏见并构建一个密集的3D重建对象级映射。我们的系统可以通过稳健的传感器和对象跟踪，可以牢固地跟踪和重建任意对象的几何形状，其语义和运动的几何形状，它们的语义和运动。此外，当对象在摄像机视野外丢失或移动时，我们的系统可以在重新观察时可靠地恢复其姿势。我们通过定量和定性测试现实世界数据序列来证明我们方法的鲁棒性和准确性。
In this paper, we present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system. Even in extremely dynamic scenes, it can robustly optimise for the camera pose, velocity, IMU biases and build a dense 3D reconstruction object-level map of the environment. Our system can robustly track and reconstruct the geometries of arbitrary objects, their semantics and motion by incrementally fusing associated colour, depth, semantic, and foreground object probabilities into each object model thanks to its robust sensor and object tracking. In addition, when an object is lost or moved outside the camera field of view, our system can reliably recover its pose upon re-observation. We demonstrate the robustness and accuracy of our method by quantitatively and qualitatively testing it in real-world data sequences.
