Open Access

ARTICLE

Real-Time Dense Reconstruction of Indoor Scene

Jinxing Niu^1,*, Qingsheng Hu¹, Yi Niu¹, Tao Zhang¹, Sunil Kumar Jha²

1 Institute of Mechanics, North China University of Water Resources and Electric Power, Zhengzhou, 450011, China
2 IT Fundamentals and Education Technologies Applications, University of Information Technology and Management in Rzeszow, Rzeszow, 100031, Poland

* Corresponding Author: Jinxing Niu. Email:

Computers, Materials & Continua 2021, 68(3), 3713-3724. https://doi.org/10.32604/cmc.2021.017418

Received 29 January 2021; Accepted 03 March 2021; Issue published 06 May 2021

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.

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