Reconstructing 3D map in indoor environment based on an improved ICP algorithm
ZHANG Yanduo1,2， YUAN Bo1,2， LI Xun1,2
1.School of Computer Science and Engineering， Wuhan Institute of Technology， Wuhan 430025；2.Hubei Key Laboratory of Intelligent robot， Wuhan Institute of Technology， Wuhan 430025
Abstract：In this paper， an improved Iterative Closest Point (ICP) algorithm is proposed based on features with the discrete selection mechanism for motion estimation to reconstruct 3D map in indoor environment. Firstly， SURF features in consecutive RGB images are extracted and matched. Then， Random Sample Consensus (RANSAC) algorithm is used for initial registration to optimize the initial pose of features and remove the outliers. Furthermore， secondary registration is applied to calculate the refined transformation between point-clouds in the different coordinate systems combining with the Iterative Closest Point algorithm which based on features with the discrete selection mechanism. Finally， the trajectory of the moving camera is optimized using General Gragh Optimization (g2o) framework combining with key frames， and 3D map is reconstructed through projecting 3D points cloud observed by the camera into global map according to current camera poses. The performance of our proposed algorithm in five public datasets is tested. The results demonstrate that the algorithm is feasible and effectively with the translational error just of 0.059m and ability to generate the 3D map of environment accurately.
2017, Vol. 51 
