A method for localizing a robot in a localization plane associated with a bi-dimentional reference with two axis x and y comprising the following steps: - determining by odometry an estimation of the coordinates x1 and y1 of the robot in the localization plane as well as an estimation of its orientation θ1 relatively to a reference direction; - determining an estimation θ2 of the orientation of the robot by using a virtual compass which identifies at least two pairs of points of interest, first points of each pair being identified in a reference panorama and second point of each pair being identified in a query panorama, this step being initialized with θ1; - determining an estimation θ3 of the orientation of the robot by correlating parts of the reference panorama with parts of the query panorama and by identifying when that correlation is maximized, this step being initialized with one of the previous estimations of the orientation; - determining an estimation x4, y4 of the robot position in the localization plane by using an Iterative Closest Points technique, this step being initialized with x1 and y1, the iterative Closest Points techniques using a 3D point cloud as an input and preliminary hypotheses in orientation; - determining the standard deviations σ_x1, σ_y1, σ_θ1 σ_θ2, σ_θ3, σ_x4, σ_y4 of the aforementioned estimations; - determining Gaussian probability distributions G, G, G(θ1), G(θ2), G(θ3), G and G of each available estimation using said standard deviations; - determining three global distributions GLOB(x), GLOB(y) and GLOB(θ) respectively for the coordinates along the x and y axis and for the orientation θ of the robot by combining said Gaussian probability distributions and determining a global estimation xg, yg of the coordinates of the robot in the localization plane as well as an global estimation θg of its orientation by applying the method of maximum likelihood to the global distributions. - determining by odometry an estimation of the coordinates x1 and y1 of the robot in the localization plane as well as an estimation of its orientation θ1 relatively to a reference direction; - determining an estimation θ2 of the orientation of the robot by using a virtual compass which identifies at least two pairs of points of interest, first points of each pair being identified in a reference panorama and second point of each pair being identified in a query panorama, this step being initialized with θ1; - determining an estimation θ3 of the orientation of the robot by correlating parts of the reference panorama with parts of the query panorama and by identifying when that correlation is maximized, this step being initialized with one of the previous estimations of the orientation; - determining an estimation x4, y4 of the robot position in the localization plane by using an Iterative Closest Points technique, this step being initialized with x1 and y1, the iterative Closest Points techniques using a 3D point cloud as an input and preliminary hypotheses in orientation; - determining the standard deviations σ_x1, σ_y1, σ_θ1 σ_θ2, σ_θ3, σ_x4, σ_y4 of the aforementioned estimations; - determining Gaussian probability distributions G, G, G(θ1), G(θ2), G(θ3), G and G of each available estimation using said standard deviations; - determining three global distributions GLOB(x), GLOB(y) and GLOB(θ) respectively for the coordinates along the x and y axis and for the orientation θ of the robot by combining said Gaussian probability distributions and determining a global estimation xg, yg of the coordinates of the robot in the localization plane as well as an global estimation θg of its orientation by applying the method of maximum likelihood to the global distributions.