Realizing　autonomous　unmanned　aerial　vehicle　(UAV)　inspection　is　of　great　significance　for　power　line　maintenance.　This　paper　introduces　a　scheme　of　using　the　structure　of　a　tower　to　realize　visual　geographical　positioning　of　UAV　for　tower　inspection　and　presents　a　monocular　semantic　simultaneous　localization　and　mapping　(SLAM)　framework　termed　PTI-SLAM　(power　tower　inspection　SLAM)　to　cope　with　the　challenge　of　a　tower　inspection　scene.　The　proposed　scheme　utilizes　prior　knowledge　of　tower　component　geolocation　and　regards　geographical　positioning　as　the　estimation　of　transformation　between　SLAM　and　the　geographic　coordinates.　To　accomplish　the　robust　positioning　and　semi-dense　semantic　mapping　with　limited　computing　power,　PTI-SLAM　combines　the　feature-based　SLAM　method　with　a　fusion-based　direct　method　and　conveys　a　loosely　coupled　architecture　of　a　semantic　task　and　a　SLAM　task.　The　fusion-based　direct　method　is　specially　designed　to　overcome　the　fragility　of　the　direct　method　against　adverse　conditions　concerning　the　inspection　scene.　Experiment　results　show　that　PTI-SLAM　inherits　the　robustness　advantage　of　the　feature-based　method　and　the　semi-dense　mapping　ability　of　the　direct　method　and　achieves　decimeter-level　real-time　positioning　in　the　airborne　system.　The　experiment　concerning　geographical　positioning　indicates　more　competitive　accuracy　compared　to　the　previous　visual　approach　and　artificial　UAV　operating,　demonstrating　the　potential　of　PTI-SLAM.