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Large-Scale Surface Reconstruction That Is Robust Against Tracking And Mapping Errors

机译:强大的表面重建功能,可防止跟踪和映射错误

摘要

Depth maps of a physical space are obtained using a depth sensor carried by a rig such as a robot or a head mounted display device worn by a user. Visible light images are also obtained. The images and orientation readings are used to create a pose graph which includes nodes connected by links. The nodes are associated with different poses of the rig and the corresponding images. Links between the nodes represent correspondences between the images, and transforms between coordinate systems of the nodes. As new images are captured, the pose graph is updated to reduce an accumulation of errors. Furthermore, surfaces in the physical space can be reconstructed at any time according to the current state of the pose graph. Volumes used in a surface reconstruction process are anchored to the nodes such that the positions of the volumes are adjusted as the pose graph is updated.
机译:使用由诸如机器人或用户佩戴的头戴式显示装置之类的装备的深度传感器来获得物理空间的深度图。还获得可见光图像。图像和方向读数用于创建姿态图,该姿态图包括通过链接连接的节点。节点与装备的不同姿势和相应的图像相关联。节点之间的链接表示图像之间的对应关系,并且在节点的坐标系之间进行转换。当捕获新图像时,姿势图将更新以减少错误的累积。此外,可以根据姿势图的当前状态随时重建物理空间中的表面。在表面重建过程中使用的体积被锚定到节点,以便在更新姿势图时调整体积的位置。

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