Aiming　at　solving　the　problem　of　blindness　in　autonomous　exploration　and　mapping　by　mobile　robots　in　unknown　environments,　an　exploration　strategy　based　on　Bayesian　optimization　to　evaluate　multiple　information　gains　was　proposed.　In　the　candidate　point　extraction　method,　the　method　of　integrating　frontier　point　clustering　and　passable　area　to　comprehensively　measure　and　extract　was　adopted.　Compared　with　the　traditional　frontier　point　detection　method,　it　effectively　solved　the　problems　of　excessive　candidate　point　sets　and　missing　environmental　information.　The　Bayesian　optimization　was　used　to　calculate　multiple　information　gains　considering　both　map　entropy　and　distance　costs.　Compared　with　the　method　of　selecting　the　best　candidate　point　based　solely　on　map　entropy,　this　method　effectively　improved　the　redundancy　path　of　the　robot　in　the　environment.　Gazebo　was　used　to　verify　the　algorithm　in　robot　operating　system　(ROS)　and　build　environment　map.　Results　show　that　the　proposed　method　can　enable　the　mobile　robot　to　explore　the　unknown　environment　quickly　and　efficiently　and　complete　the　mapping　task　with　high　quality.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.