The　firing　of　space　cell　in　hippocampus　is　considered　to　be　able　to　form　an　intrinsic　map　for　the　environment　which　is　called　cognitive　map.　Previous　bionic　navigation　algorithms　(such　as　rat　simultaneous　localization　and　mapping)　and　traditional　SLAM　algorithms　lack　sufficient　physiological　basis　and　cannot　reflect　the　cognitive　mechanism　of　hippocampal　formation.　Based　on　the　cognitive　mechanism　of　hippocampal　space　cells,　this　paper　proposes　a　navigation　algorithm　for　constructing　accurate　environmental　cognitive　map.　This　algorithm　is　characterized　by　the　construction　of　a　unified　spatial　cell　attractor　model　for　self-motion　trajectory　path　integration.　The　expressions　of　grid　cells　and　place　cells　are　driven　by　stripe　cells.　The　algorithm　performs　closed-loop　detection　by　collecting　depth　image,　red　green　blue+depthmap　information　and　corrects　the　error　of　spatial　cells＇　path　integral.　Eventually,　we　get　a　precise　cognitive　map　of　the　environment　and　bionic　robot　navigation　is　achieved　by　global　navigation　and　local　navigation　algorithm　based　on　the　map.　The　cognitive　map　is　a　topological　metric　map　that　contains　the　topological　relations　of　the　environment　feature　point　coordinates,　visual　cues,　and　specific　sites.　In　this　paper,　the　method　is　verified　by　the　simulation　experiment　and　the　physical　experiment　on　the　robot　platform.　The　research　results　laid　the　foundation　for　the　research　of　the　robot　navigation　method　based　on　the　hippocampus　cognitive　mechanism.　Note　to　Practitioners　Environmental　cognition　and　navigation　ability　are　the　main　function　of　intelligent　mobile　robots.　People　and　animals　have　strong　adaptability　and　cognitive　ability　in　unfamiliar　environment,　and　they　can　independently　recognize　and　navigate　in　unfamiliar　environment.　Now,　the　environmental　cognition　and　navigation　ability　of　intelligent　robots　are　far　from　the　level　of　human　and　animal.　This　paper　suggests　a　bionic　robot　navigation　algorithm　based　on　the　cognitive　mechanism　of　rat　hippocampus.　The　bionic　robot　navigation　algorithm　makes　mobile　robots　more　intelligent　by　learning　and　imitating　human　and　animal＇s　environmental　cognition　and　navigation　ability.　This　navigation　algorithm　conforms　to　the　physiological　characteristics　and　is　pretty　accurate　and　intelligent.　The　experimental　results　demonstrate　the　effectiveness　of　this　algorithm　in　building　environment　map　and　navigation.