A computer implemented method for locating a vehicle/robot, comprising: Reading a Signatured Gaussian Mixture Map for a map section which the vehicle/robot is currently located in, wherein the Signatured Gaussian Mixture Map comprises Signatured Gaussian Mixture Models for map elements within the map section; generating Signatured Gaussian Mixture Models for map elements within a real-time point cloud or an image acquired by the vehicle/robot; establishing one or more correspondences between the Signatured Gaussian Mixture Map and the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image based on signatures of the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image; and matching the Signatured Gaussian Mixture Map with the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image based on the one or more correspondences established; wherein "generating Signatured Gaussian Mixture Models for map elements within a real-time point cloud or an image acquired by the vehicle/robot" comprises: - generating a Gaussian Mixture Model for the map element by using Gaussian Mixture Models of geometric primitives to approximate a shape of the map element; - generating a signature for identifying the map element, wherein the signature comprises properties of the map element; and - generating a Signatured Gaussian Mixture Model for representing the map element, wherein the Signatured Gaussian Mixture Model comprises the Gaussian Mixture Model, the signature and an existence probability of the map element. Reading a Signatured Gaussian Mixture Map for a map section which the vehicle/robot is currently located in, wherein the Signatured Gaussian Mixture Map comprises Signatured Gaussian Mixture Models for map elements within the map section; generating Signatured Gaussian Mixture Models for map elements within a real-time point cloud or an image acquired by the vehicle/robot; establishing one or more correspondences between the Signatured Gaussian Mixture Map and the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image based on signatures of the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image; and matching the Signatured Gaussian Mixture Map with the Signatured Gaussian Mixture Models for the map elements within the real-time point cloud or image based on the one or more correspondences established; wherein "generating Signatured Gaussian Mixture Models for map elements within a real-time point cloud or an image acquired by the vehicle/robot" comprises: - generating a Gaussian Mixture Model for the map element by using Gaussian Mixture Models of geometric primitives to approximate a shape of the map element; - generating a signature for identifying the map element, wherein the signature comprises properties of the map element; and - generating a Signatured Gaussian Mixture Model for representing the map element, wherein the Signatured Gaussian Mixture Model comprises the Gaussian Mixture Model, the signature and an existence probability of the map element. - generating a Gaussian Mixture Model for the map element by using Gaussian Mixture Models of geometric primitives to approximate a shape of the map element; - generating a signature for identifying the map element, wherein the signature comprises properties of the map element; and - generating a Signatured Gaussian Mixture Model for representing the map element, wherein the Signatured Gaussian Mixture Model comprises the Gaussian Mixture Model, the signature and an existence probability of the map element.