Motivated by the use of large-scale surface topography measurement by robots, we propose a method of point-cloud splicing based on the indoor global positioning system (iGPS). In our research, the iGPS world coordinate system is utilized as the coordinate system of point-cloud splicing to establish a mathematical model of point-cloud splicing. Furthermore, we employ the particle swarm optimization (PSO) algorithm for the iterative closest point (ICP) algorithm. The experimental results of point-cloud splicing of spherical distance measurement show that the accuracy of the measurement system is less than 0.1 mm. We also conduct a front-bumper point-cloud splicing experiment and the experimental result denote that the maximum negative deviation is -0.05189 mm and the maximum positive deviation is 0.0727 mm, which are less than 0.1 mm. It is also found that the deviation distribution is relatively uniform, which validates the proposed algorithm has a good effect on large-scale point-cloud splicing.