To　satisfy　the　distinctive　feature　extraction　requirement　of　one-shot　learning　gesture　recognition　for　mobile　robot　control,　a　improved　three-dimensional　local　sparse　motion　scale　invariant　feature　transform　(3D　SMoSIFT)　feature　descriptor　is　proposed,　which　fuses　RGB-D　videos.　Firstly,　gray　pyramid,　depth　pyramid　and　optical　flow　pyramids　are　built　as　scale　space　for　each　gray　frame　(converted　from　RGB　frame)　and　depth　frame.　Then　interest　regions　are　extracted　according　the　variance　of　optical　flow,　and　variance　is　calculated　in　horizontal　and　vertical　direction.　Subsequently,　corners　are　just　extracted　in　each　interest　region　as　interest　points,　and　then　the　information　of　gray　and　depth　optical　flow　is　simultaneously　used　to　detect　robust　keypoints　around　the　motion　pattern　in　the　scale　space.　Finally,　SIFT　descriptors　are　calculated　on　3D　gradient　space　and　3D　motion　space.　The　improved　feature　descriptor　has　been　evaluated　under　a　hag　of　feature　model　on　one-shot　learning　Chaleam　Gesture　Dataset.　Experiments　demonstrate　that　the　proposed　method　distinctly　improves　the　accuracy　of　gesture　recognition.　The　results　also　show　that　the　improved　3D　SMoSIFT　feature　descriptor　surpasses　other　spatiotemporal　feature　descriptors　and　is　comparable　to　the　state-of-the-art　approaches.