Fusion Key Frame Image Confidence Assessment of the Medical Service Robot Whole Scene Reconstruction

Abstract

Facing the precise service and emergency rescue needs of medical service robots in irregular scene, in order to achieve better navigation and path planning for robots in service scenarios, for the whole reconstruction of the absolute scale service scenario, this article proposes a frame of whole scene three-dimensional (3D) point cloud reconstruction based on the fusion of scene depth estimation, confidence assessment, and pose tracking with monocular camera. The algorithm first collects the scene focus stack images under an initial viewing angle through the robot mobile terminal of camera. The absolute depth information of the scene is estimated on the server side, and the confidence level of the reconstructed image of the point cloud is evaluated, and non- uniform sampling is performed to reduce the influence of the error estimation. Based on the sparse key frame position information defined by monocular SLAM, the 3D reconstruction of the whole scene in absolute scale is realized through multi-perspective point cloud pose matching. It provides information of cloud reconstruction of scenic spots for target recognition and navigation of a medical service robot.