Deep　reinforcement　learning　has　achieved　great　success　in　laser-based　collision　avoidance　works　because　the　laser　can　sense　accurate　depth　information　without　too　much　redundant　data,　which　can　maintain　the　robustness　of　the　algorithm　when　it　is　migrated　from　the　simulation　environment　to　the　real　world.　However,　high-cost　laser　devices　are　not　only　difficult　to　deploy　for　a　large　scale　of　robots　but　also　demonstrate　unsatisfactory　robustness　towards　the　complex　obstacles,　including　irregular　obstacles,　e.g.,　tables,　chairs,　and　shelves,　as　well　as　complex　ground　and　special　materials.　In　this　paper,　we　propose　a　novel　monocular　camera-based　complex　obstacle　avoidance　framework.　Particularly,　we　innovatively　transform　the　captured　RGB　images　to　pseudo-laser　measurements　for　efficient　deep　reinforcement　learning.　Compared　to　the　traditional　laser　measurement　captured　at　a　certain　height　that　only　contains　one-dimensional　distance　information　away　from　the　neighboring　obstacles,　our　proposed　pseudo-laser　measurement　fuses　the　depth　and　semantic　information　of　the　captured　RGB　image,　which　makes　our　method　effective　for　complex　obstacles.　We　also　design　a　feature　extraction　guidance　module　to　weight　the　input　pseudo-laser　measurement,　and　the　agent　has　more　reasonable　attention　for　the　current　state,　which　is　conducive　to　improving　the　accuracy　and　efficiency　of　the　obstacle　avoidance　policy.　Besides,　we　adaptively　add　the　synthesized　noise　to　the　laser　measurement　during　the　training　stage　to　decrease　the　sim-to-real　gap　and　increase　the　robustness　of　our　model　in　the　real　environment.　Finally,　the　experimental　results　show　that　our　framework　achieves　state-of-the-art　performance　in　several　virtual　and　real-world　scenarios.