Before　the　cognitive　map　is　generated　through　the　fire　of　the　rodent　hippocampal　spatial　cells,　mammals　can　obtain　the　outside　knowledge　through　the　visual　information,　which　comes　from　the　eyeball　to　the　brain.　The　information　is　encoded　and　transferred　to　the　two　regions　of　the　brain　based　on　the　fact　of　biophysiological　research,　which　are　known　as　＂what＂　loop　and　＂where＂　loop.　In　this　article,　we　simulate　an　episodic　memory　recognition　unit　consisting　of　the　integration　of　two-loop　information,　which　is　applied　to　building　the　accurate　bioinspired　spatial　cognitive　map　of　real　environments.　We　employ　the　visual　bag　of　word　algorithm　based　on　oriented　Feature　from　Accelerated　Segment　Test　and　rotated　Binary　Robust　Independent　Elementary　Features　feature　to　build　the　＂what＂　loop　and　the　hippocampal　spatial　cells　cognitive　model,　which　comes　from　the　front-end　visual　information　input　system　to　build　the　＂where＂　loop.　At　the　same　time,　the　environmental　cognitive　map　is　a　topological　map　containing　information　about　place　cell　competition　firing　rate,　oriented　Feature　from　Accelerated　Segment　Test　and　rotated　Binary　Robust　Independent　Elementary　Features　feature　descriptor,　similarity　of　image　retrieval,　and　relative　location　of　cognitive　map　nodes.　The　simulation　experiments　and　physical　experiments　in　a　mobile　robot　platform　have　been　done　to　verify　the　environmental　adaptability　and　robustness　of　the　algorithm.　This　proposing　algorithm　would　provide　a　foundation　for　further　research　on　bioinspired　navigation　of　robots.