The　safe　operation　and　efficient　decision-making　of　the　ground　unmanned　platform　rely　on　accurate　localization　of　the　platform　and　overall　perception　of　the　environment.　For　the　problem　of　limited　perception　of　single-view　robots,　a　laser　SLAM　(simulation　localization　and　mapping)　system　based　on　the　ground-to-air-view　information　fusion　is　proposed.　Firstly,　the　aerial　point　cloud　map　constructed　by　the　UAV　(unmanned　aerial　vehicle)　is　introduced　as　the　prior　information　in　the　system.　Then,　the　optimal　registration　from　the　aerial　submap　to　the　ground　local　map　is　obtained　through　the　registration　network　for　ground-air　point　cloud　submaps.　After　that,　the　aerial　prior　information　and　the　ground　perception　information　are　fused　based　on　the　multi-view　graph　optimization　framework.　Finally,　experiments　are　carried　out　on　a　road　about　1000　m　long　in　a　construction　site.　The　average　translation　error　of　the　proposed　system　is　reduced　by　5.87　m　compared　with　the　classic　single-view　laser　SLAM　system,　while　the　average　rotation　error　is　reduced　by　1.67◦.　The　results　show　that　the　proposed　method　effectively　improves　the　localization　accuracy　of　the　ground　unmanned　platform.　In　addition,　the　perceptual　blind　areas　of　ground　unmanned　platforms,　caused　by　the　intersection　structure,　the　occlusion　of　obstacles,　etc.,　are　effectively　made　up　by　map　fusion　in　the　proposed　system.　©　2023　Chinese　Academy　of　Sciences.　All　rights　reserved.