This　paper　proposes　a　novel　stereo　matching　algorithm　to　solve　environment　sensing　problems.　It　integrates　a　non-convex　optical　flow　and　Viterbi　process.　The　non-convex　optical　flow　employs　a　new　adaptive　weighted　non-convex　Total　Generalized　Variation　(TGV)　model,　which　can　obtain　sharp　disparity　maps.　Structural　similarity,　total　variation　constraint,　and　a　specific　merging　strategy　are　combined　with　the　4　bi-directional　Viterbi　process　to　improve　the　robustness.　In　the　fusion　of　the　optical　flow　and　Viterbi　process,　a　new　occlusion　processing　method　is　incorporated　in　order　to　get　more　sharp　disparity　and　more　robust　result.　Extensive　experiments　are　conducted　to　compare　this　algorithm　with　other　state-of-the-art　methods.　Experimental　results　show　the　superiority　of　our　algorithm.