Virtual Reality (VR) has recently experienced rapid development for human-computer interactions. Users wearing VR headsets gain an immersive experience when interacting with a 3-dimensional (3D) world. We utilise a light detection and ranging (LiDAR) sensor to detect a 3D point cloud from the real world. To match the scale between a virtual environment and a user's real world, this paper develops a boundary wall detection method using the Hough transform algorithm. A connected-componentlabelling (CCL) algorithm is applied to classify the Hough space into several distinguishable blocks that are segmented using a threshold. The four largest peaks among the segmented blocks are extracted as the parameters of the wall plane. The virtual environment is scaled to the size of the real environment. In order to synchronise the position of the user and his/her avatar in the virtual world, a wireless Kinect network is proposed for user localisation. Multiple Kinects are mounted in an indoor environment to sense the user's information from different viewpoints. The proposed method supports the omnidirectional detection of the user's position and gestures. To verify the performance of our proposed system, we developed a VR game using several Kinects and a Samsung Gear VR device.