Traditional　indoor　laser　scanning　trolley/backpacks　with　multi-laser　scanner,　panorama　cameras,　and　an　inertial　measurement　unit　(IMU)　installed　are　a　popular　solution　to　the　3D　indoor　mapping　problem.　However,　the　cost　of　those　mapping　suits　is　quite　expensive,　and　can　hardly　be　replicated　by　consumer　electronic　components.　The　consumer　RGB-Depth　(RGB-D)　camera　(e.g.,　Kinect　V2)　is　a　low-cost　option　for　gathering　3D　point　clouds.　However,　because　of　the　narrow　field　of　view　(FOV),　its　collection　efficiency　and　data　coverages　are　lower　than　that　of　laser　scanners.　Additionally,　the　limited　FOV　leads　to　an　increase　of　the　scanning　workload,　data　processing　burden,　and　risk　of　visual　odometry　(VO)/simultaneous　localization　and　mapping　(SLAM)　failure.　To　find　an　efficient　and　low-cost　way　to　collect　3D　point　clouds　data　with　auxiliary　information　(i.e.,　color)　for　indoor　mapping,　in　this　paper　we　present　a　prototype　indoor　mapping　solution　that　is　built　upon　the　calibration　of　multiple　RGB-D　sensors　to　construct　an　array　with　large　FOV.　Three　time-of-flight　(ToF)-based　Kinect　V2　RGB-D　cameras　are　mounted　on　a　rig　with　different　view　directions　in　order　to　form　a　large　field　of　view.　The　three　RGB-D　data　streams　are　synchronized　and　gathered　by　the　OpenKinect　driver.　The　intrinsic　calibration　that　involves　the　geometry　and　depth　calibration　of　single　RGB-D　cameras　are　solved　by　homography-based　method　and　ray　correction　followed　by　range　biases　correction　based　on　pixel-wise　spline　line　functions,　respectively.　The　extrinsic　calibration　is　achieved　through　a　coarse-to-fine　scheme　that　solves　the　initial　exterior　orientation　parameters　(EoPs)　from　sparse　control　markers　and　further　refines　the　initial　value　by　an　iterative　closest　point　(ICP)　variant　minimizing　the　distance　between　the　RGB-D　point　clouds　and　the　referenced　laser　point　clouds.　The　effectiveness　and　accuracy　of　the　proposed　prototype　and　calibration　method　are　evaluated　by　comparing　the　point　clouds　derived　from　the　prototype　with　ground　truth　data　collected　by　a　terrestrial　laser　scanner　(TLS).　The　overall　analysis　of　the　results　shows　that　the　proposed　method　achieves　the　seamless　integration　of　multiple　point　clouds　from　three　Kinect　V2　cameras　collected　at　30　frames　per　second,　resulting　in　low-cost,　efficient,　and　high-coverage　3D　color　point　cloud　collection　for　indoor　mapping　applications.