Loop　closure　detection　(LCD)　is　an　important　module　of　visual　SLAM　(Simultaneous　Localization　and　Mapping),　which　can　be　regarded　as　an　image　retrieval　task.　The　correct　LCD　can　effectively　reduce　the　cumulative　errors　during　visual　SLAM　localization　and　obtain　a　globally　consistent　map.　The　currently　common　approach　to　LCD　is　based　on　a　bag-of-words　(BoW)　and　traditional　manually　designed　features.　However,　The　BoW-based　method　ignores　the　spatial　location　information　and　semantic　information　of　visual　features,　and　image　aliasing　will　affect　the　detection　accuracy　of　the　algorithm.　Meanwhile,　the　artificially　designed　features　are　very　sensitive　to　light　changes.　This　paper　proposes　a　LCD　algorithm　based　on　convolutional　neural　networks　(CNN)　to　extract　global　and　local　features.　Extensive　experiments　on　six　publicly　available　datasets　have　shown　that　the　method　provides　high　recall　with　100%　accuracy　in　most　of　the　evaluated　datasets　relative　to　traditional　algorithms　and　some　deep　learning-based　methods.　©　2022　IEEE.