With　the　increasingly　widespread　application　of　unmanned　aerial　vehicle　(UAV),　safety　issues　such　as　effectiveness　of　obstacle　avoidance　have　been　paid　more　attentions.　The　classical　obstacle　avoidance　algorithms　are　mostly　suitable　for　mobile　robots,　but　these　algorithms　are　not　ideal　for　UAV　using　in　three-dimensional　space.　Most　of　the　three-dimensional　obstacle　avoidance　algorithms　which　are　more　effective　using　RGB　image　data　as　input.　Thus,　a　large　amount　of　image　data　is　involved　in　complex　computing　process.　This　study　proposes　an　effective　obstacle　avoidance　algorithm　for　UAV　with　less　input　data　and　fewer　sensors　based　on　RealSense　and　reinforcement　learning.　It　combines　the　feature　map　of　the　depth　image　of　RealSense　as　the　input　data　of　reinforcement　learning　and　the　current　direction　of　flight　of　UAV　to　calculate　the　direction　and　angle　of　avoiding.　The　proposed　algorithm　that　implements　real-time　obstacle　avoidance　for　UAV　has　been　verified　by　simulation　and　tested　in　three-dimensional　space　scenario.　©　2020　Institution　of　Engineering　and　Technology.　All　rights　reserved.