@Article{cmc.2021.017418,
AUTHOR = {Jinxing Niu, Qingsheng Hu, Yi Niu, Tao Zhang, Sunil Kumar Jha},
TITLE = {Real-Time Dense Reconstruction of Indoor Scene},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {3},
PAGES = {3713--3724},
URL = {http://www.techscience.com/cmc/v68n3/42506},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2021.017418}
}