Real-Time Dense Reconstruction of Indoor Scene

Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots,augmented reality,cultural relics conservation and other fields.ORB-SLAM2 method is one of the excellent open source algorithms in visual SLAM system,which is often used in indoor scene reconstruction.However,it is time-consuming and can only build sparse scene map by using ORB features to solve camera pose.In view of the shortcomings of ORB-SLAM2 method,this article proposes an improved ORB-SLAM2 solution,which uses a direct method based on light intensity to solve the camera pose.It can greatly reduce the amount of computation,the speed is significantly improved by about 5 times compared with the ORB feature method.A parallel thread of map reconstruction is added with surfel model,and depth map and RGB map are fused to build the dense map.A Realsense D415 sensor is used as RGB-D cameras to obtain the three-dimensional(3D)point clouds of an indoor environments.After calibration and alignment processing,the sensor is applied in the reconstruction experiment of indoor scene with the improved ORB-SLAM2 method.Results show that the improved ORB-SLAM2 algorithm cause a great improvement in processing speed and reconstructing density of scenes.