For　autonomous　robots,　3D　perception　of　environment　is　an　essential　tool,　which　can　be　used　to　achieve　better　navigation　in　an　obstacle　rich　environment.　This　understanding　requires　a　huge　amount　of　computational　resources;　therefore,　the　real-time　3D　reconstruction　of　surrounding　environment　has　become　a　topic　of　interest　for　countless　researchers　in　the　recent　past.　Generally,　for　the　outdoor　3D　models,　stereo　cameras　and　laser　depth　measuring　sensors　are　employed.　The　data　collected　through　the　laser　ranging　sensors　is　relatively　accurate　but　sparse　in　nature.　In　this　paper,　we　propose　a　novel　mechanism　for　the　incremental　fusion　of　this　sparse　data　to　the　dense　but　limited　ranged　data　provided　by　the　stereo　cameras,　to　produce　accurate　dense　depth　maps　in　real-time　over　a　resource　limited　mobile　computing　device.　Evaluation　of　the　proposed　method　shows　that　it　outperforms　the　state-of-the-art　reconstruction　frameworks　which　only　utilizes　depth　information　from　a　single　source.