Abstract: A novel RGB-D based feature point depth constraint and locality constraint integrated real-time indoor scene 3D reconstruction algorithm is proposed， which focusing to solve problems such as the inaccuracy of 3D point cloud matching， excessive time consuming and the loss of point depth. Firstly， the feature points are detected using Harris point detector， and then labeled with 64 dimensional vector using SURF descriptor. Secondly， the initial correct feature point pairs are selected between the successive frames with the depth information constraint and feature point locality constraint in addition to vector similarity constraint. Thirdly， the outliers are removed and the camera pose is estimated based on the random sample consensus(RANSAC) method. Eventually， 3D point clouds are determined using the general graph optimization (g2o) facilitating the indoor scene reconstruction. In the experiments， the RGB-D camera is fixed on the automatic guided vehicle to capture the indoor surrounding scenes. Experimental results validate that the proposed approach is feasible and effective.

Key words: RGB-D, 3D reconstruction, feature point depth constraint, feature point locality constraint, instantaneity