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.