Abstract: A method of localizing a mobile body (MB) in a known environment, includes the following steps: a) defining an occupancy grid (G) modeling the environment; b) defining a set of position grids (?) each position grid being associated to a heading of the mobile body; c) receiving a time series of measurements (z1, z2, . . . ) from a distance sensor carried by the mobile body; 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 the occupancy probabilities and of the received measurement; wherein step d) is carried out by applying an inverse sensor model to the received measurement, while considering the distance sensor co-located with a detected obstacle and by applying Bayesian fusion to update the pose probabilities of the position grids.

Abstract: An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells.

Abstract: A method of localizing a mobile body (MB) in a known environment, includes the following steps: a) defining an occupancy grid (G) modeling the environment; b) defining a set of position grids (?) each position grid being associated to a heading of the mobile body; c) receiving a time series of measurements (z1, z2, . . . ) from a distance sensor carried by the mobile body; 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 the occupancy probabilities and of the received measurement; wherein step d) is carried out by applying an inverse sensor model to the received measurement, while considering the distance sensor co-located with a detected obstacle and by applying Bayesian fusion to update the pose probabilities of the position grids.

Abstract: A method of performing simultaneous localization of a mobile body and mapping of its environment, includes the steps of: a) defining an occupancy grid (G) on a region of the environment and a pose grid (?) comprising for the mobile body; b) receiving a first time series of distance measurements (z1, z2) and, upon reception of a distance measurement: either b1) updating occupancy probabilities of the occupancy grid; or b2) updating pose probabilities of the pose grid; and c) receiving a second time series of odometry measurements (u1, u2), each of the measurements being representative of a motion of the mobile body in the environment and, upon reception of an odometry measurement, updating the pose probabilities of the pose grid. An apparatus for carrying out such a method is also provided.

Abstract: An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells.