A method of localizing a mobile body in a known environment, the mobile body carrying at least one distance sensor, the method comprising the following steps, carried out by a data processor: a) defining an occupancy grid modeling the environment, the grid comprising a plurality of cells, each cell of the occupancy grid being either in an 'occupied' state if it contains an obstacle or in an 'empty' state if it does not contains any obstacle; b) defining a set of position grids (Π α 1, Π α 2,...), each position grid being associated to a heading (α) of the mobile body and comprising a plurality of cells ( C 1 Π, C 2 Π,...), a pose probability of the mobile body being associated to each cell of each position grid, a pose corresponding to the association of a position and a heading of the mobile body, the occupancy grid and the position grids being defined at least over a same region of space; c) receiving a time series of measurements (z 1, z 2,...) from the distance sensor, each of said measurements being representative of a distance between the sensor and a nearest obstacle on its line-of-view (LoV); and d) upon receiving a measurement of the time series, updating the pose probabilities of the position grids as a function of present values of said pose probabilities and of the received measurement; the method characterized by: a step a') of identifying, in the occupancy grid modeling the environment, cells in the 'occupied' states bordering at least one cell in the 'empty' state, said cells being called 'source' cells; and a step a") of defining, for every 'source' cell, a local occupancy grid (LOG 1, LOG 2...) comprising a plurality of cells, an occupancy probability being associated to each cell of the local occupancy grid; wherein step d) comprises: d1) for all or at least a subset of the 'source' cells, computing an occupancy probability of the cells of the corresponding local occupancy grid by applying an inverse sensor model to the measurement, while considering the distance sensor located in the 'source' cell; d2) projecting the local occupancy grids onto a global occupancy grid and performing Bayesian fusion of the corresponding occupancy probability; and d3) updating the pose probabilities of the position grids by performing Bayesian fusion with the occupancy probabilities of the global occupancy grid. a) defining an occupancy grid modeling the environment, the grid comprising a plurality of cells, each cell of the occupancy grid being either in an 'occupied' state if it contains an obstacle or in an 'empty' state if it does not contains any obstacle; b) defining a set of position grids (Π α 1, Π α 2,...), each position grid being associated to a heading (α) of the mobile body and comprising a plurality of cells ( C 1 Π, C 2 Π,...), a pose probability of the mobile body being associated to each cell of each position grid, a pose corresponding to the association of a position and a heading of the mobile body, the occupancy grid and the position grids being defined at least over a same region of space; c) receiving a time series of measurements (z 1, z 2,...) from the distance sensor, each of said measurements being representative of a distance between the sensor and a nearest obstacle on its line-of-view (LoV); and d) upon receiving a measurement of the time series, updating the pose probabilities of the position grids as a function of present values of said pose probabilities and of the received measurement; a step a') of identifying, in the occupancy grid modeling the environment, cells in the 'occupied' states bordering at least one cell in the 'empty' state, said cells being called 'source' cells; and a step a") of defining, for every 'source' cell, a local occupancy grid (LOG 1, LOG 2...) comprising a plurality of cells, an occupancy probability being associated to each cell of the local occupancy grid; wherein step d) comprises: d1) for all or at least a subset of the 'source' cells, computing an occupancy probability of the cells of the corresponding local occupancy grid by applying an inverse sensor model to the measurement, while considering the distance sensor located in the 'source' cell; d2) projecting the local occupancy grids onto a global occupancy grid and performing Bayesian fusion of the corresponding occupancy probability; and d3) updating the pose probabilities of the position grids by performing Bayesian fusion with the occupancy probabilities of the global occupancy grid. d1) for all or at least a subset of the 'source' cells, computing an occupancy probability of the cells of the corresponding local occupancy grid by applying an inverse sensor model to the measurement, while considering the distance sensor located in the 'source' cell; d2) projecting the local occupancy grids onto a global occupancy grid and performing Bayesian fusion of the corresponding occupancy probability; and d3) updating the pose probabilities of the position grids by performing Bayesian fusion with the occupancy probabilities of the global occupancy grid.