A method for processing high-precision map data, the method comprising: performing (S101) at least twice collection on map data along a target trajectory so as to respectively acquire at least twice original map data, wherein the at least twice collection is performed by a collection tool, and the collection tool comprises at least one of an inertial measurement unit (IMU), and a global positioning system (GPS); recognizing (S102) an object in the original map data respectively and extracting an object feature, wherein the recognizing an object in the original map data respectively and extracting an object feature comprises: performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object, wherein the performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object comprises: clustering point cloud data having reflectivity reaching a high reflectivity condition based on a reflectivity of the point cloud data in the original map data, performing plane fitting on the clustered point cloud data, recognizing a road sign from the fitted plane and extracting a planar feature of the road sign; matching the planar feature of the road sign in the original map data collected at each time; taking the road sign with the matched planar feature as a reference object; and fusing the original map data collected at each time by utilizing a relative position relationship of the reference object. performing (S101) at least twice collection on map data along a target trajectory so as to respectively acquire at least twice original map data, wherein the at least twice collection is performed by a collection tool, and the collection tool comprises at least one of an inertial measurement unit (IMU), and a global positioning system (GPS); recognizing (S102) an object in the original map data respectively and extracting an object feature, wherein the recognizing an object in the original map data respectively and extracting an object feature comprises: performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object, wherein the performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object comprises: clustering point cloud data having reflectivity reaching a high reflectivity condition based on a reflectivity of the point cloud data in the original map data, performing plane fitting on the clustered point cloud data, recognizing a road sign from the fitted plane and extracting a planar feature of the road sign; matching the planar feature of the road sign in the original map data collected at each time; taking the road sign with the matched planar feature as a reference object; and fusing the original map data collected at each time by utilizing a relative position relationship of the reference object. performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object, wherein the performing recognition on a static object using point cloud data in the original map data and extracting a structural feature of the static object comprises: clustering point cloud data having reflectivity reaching a high reflectivity condition based on a reflectivity of the point cloud data in the original map data, performing plane fitting on the clustered point cloud data, recognizing a road sign from the fitted plane and extracting a planar feature of the road sign; matching the planar feature of the road sign in the original map data collected at each time; taking the road sign with the matched planar feature as a reference object; and fusing the original map data collected at each time by utilizing a relative position relationship of the reference object. <EOS>