With the rise of digital twins and smart cities, Building Information Modelling have been widely adopted by the construction industry from design, construction to operation maintenance. We present a BIPS (Building Information Positioning System) method which integrates a smartphone VPS (visual positioning system) based on the BIM models, and VO (visual odometry) for the indoor positioning. Firstly, the smartphone images and sensor measurements are sent to a server. In the server, the VPS utilizes computer vision algorithms to extract semantics from the smartphone images. Then, the smartphone image semantics are compared with the BIM semantics. The hypothesized position candidates are distributed in the BIM model. The candidate with the maximum likelihood is regarded as the VPS heading and position estimation. An extended Kalman filter is then used to integrate the VPS with VO, where the former and latter provide measurement and propagation models, respectively. According to the simulation result, the proposed BIPS proves effective in an indoor environment, being capable of improving indoor positioning accuracy to about 1 meter.