A method for perceiving physical bodies comprises the following steps: a) acquisition of a plurality of distance measurements of the physical bodies, arising from one or more sensor; b) application of an inverse sensor model on an occupancy grid to determine a probability of occupancy of a set of cells of the grid; and c) construction of a consolidated occupancy grid by fusion of the occupancy probabilities estimated during step b); wherein the inverse sensor model is a discrete model, associating with each cell of the corresponding occupancy grid, and for each distance measurement, a probability class chosen inside one and the same set of finite cardinality and identified by an integer index; and wherein step c) is implemented by means of integer computations performed on the indices of the probability classes determined during said step b). A system for perceiving physical bodies is also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for perceiving physical bodies (O) comprising the following steps, implemented by a computer or a dedicated digital electronic circuit (MTD1, MTD2): a) acquisition of a plurality of distance measurements (z1 . . . zNC) of said physical bodies, arising from one or more sensors (C1 . . . CNC); b) application, to each said distance measurement, of an inverse model of the corresponding sensor on an occupancy grid (GO) providing a discretized spatial representation of an environment of said sensor, so as to determine a probability of occupancy by a physical body of a set of cells of said occupancy grid; and c) construction of a consolidated occupancy grid, each cell of which exhibits an occupancy probability computed by fusing the occupancy probabilities estimated during step b); wherein each said inverse sensor model is a discrete model, associating with each cell of the corresponding occupancy grid, and for each distance measurement, a probability class chosen inside one and the same set of finite cardinality, each said probability class being identified by an integer index; and wherein, during said step c), the probability of occupancy of each cell of the consolidated occupancy grid is determined by means of integer computations performed on the indices of the probability classes determined during said step b). 2. The method as claimed in claim 1 wherein said set of finite cardinality of probability classes is formed by the union of one or more subsets such that, during said step c), the fusion of two probability classes belonging to one and the same subset provides a result also belonging to said subset. 3. The method as claimed in claim 1, wherein said set of finite cardinality of probability classes constitutes a non-uniform discretization of the probability interval [0, 1], with a discretization spacing increasing between 0 and 0.5, and then decreasing between 0.5 and 1. 4. The method as claimed in claim 2, wherein said set, designated by Sk, of finite cardinality of probability classes is formed by the union of two subsets Sk− and Sk+ defined by: S k - = { p n = 1 2 - k · n , n ≤ 0 } S k + = { p n = k · n + 1 k · n + 2 , n ≥ 0 } the index n taking relative integer values and the parameter k being a non-zero relative integer, and wherein, during said step c), the fusion F(pi, pj) between two probability classes pi, pjϵSk is computed by applying the following equations when the indices “i” and “j” have the same sign: F(pi, pj)=pi+j−k·i·j if i,j<0; F(pi, pj)=pi+j+k·i·j if i,j>0. 5. The method as claimed in claim 4, wherein, during said step c), the fusion F(pi, pj) between two probability classes pi, pjϵSk is computed by applying the following equations when the indices “i” and “j” have opposite signs: F(pi, pj)=p(i+j)÷(1+k·j) if i<0, j>0 and |i|≥|j|; F(pi, pj)−p(i+j)÷(1−k·i) if i<0, j>0 and |i|≤|j|. where the symbol ÷ designates an integer division. 6. The method as claimed in claim 4, wherein k=1. 7. The method as claimed in claim 3, wherein said set, designated by Gp, of finite cardinality of probability classes is formed by the union of two subsets Gp− and Gp+ defined by: Gp−={(pn), n≤0} Gp+={(pn), n≥} the index n taking relative integer values, p being a parameter of value lying strictly between 0.5 and 1 and the probability pn being defined by recursion in the following manner: p0=0.5; p1=p; pn+1=F(pn, p) ∀n>1 p−1=1−p; pn−1=F(pn, p−1)∀n<−1 and wherein, during said step c), the fusion F(pi, pj) between two probability classes pi, pjϵGp is computed by applying the following equation: F(pi, pj)=pi+j 8. The method as claimed in claim 1, wherein said step c) comprises the implementation of changes of frame to construct said consolidated occupancy grid on the basis of probabilities of occupancy of cells of occupancy grids associated with non-co-located sensors. 9. The method as claimed in claim 1, also comprising a preliminary step of constructing the inverse models of at least one said sensor on the corresponding occupancy grid, implemented by applying the following equation: where: P(oi|z) represents the probability of occupancy of the cell of index “i” of the occupancy grid, said cells being ordered by increasing distance from said sensor; N is the number of cells of said occupancy grid; z represents a distance measurement arising from the sensor; and p(z|xk) represents the direct model of the sensor, expressing the probability density of the measurement “z” provided by the sensor when the position xk of the cell k of the occupancy grid, but no other cell closer to the sensor, is occupied by a physical body. 10. The method as claimed in claim 1, also comprising a preliminary step of determining a spatial resolution of at least one said occupancy grid, said spatial resolution being chosen the smallest compatible with the constraint max[p(z|xk)]≥pmin, where p(z|xk) is a direct model of the sensor, expressing the probability density of the measurement “z” provided by the sensor when the cell xk of the occupancy grid, but no other cell closer to the sensor, is occupied by a physical body, and pmin is the smallest probability class belonging to said set and greater than 0.5. 11. A system for perceiving physical bodies comprising: at least one input port for receiving a plurality of signals (z1, . . . , zNC) representative of distance measurements of said physical bodies, arising from one or more sensors; a data processing module (MTD1, MTD2) configured to receive as input said signals and to use them to construct a consolidated occupancy grid by applying a method as claimed in claim 1; and at least one output port for a signal (gfus) representative of said consolidated occupancy grid. 12. The system as claimed in claim 11, also comprising one or more distance sensors (C1, . . . , CNC) adapted to generate signals representative of a plurality of distance measurements of said physical bodies and linked to said input port or ports. 13. The system as claimed in claim 11, wherein said data processing module comprises at least one hardware block for computing occupancy probabilities (CO1, . . . , COnNC) comprising a memory storing, in the form of a correspondence table, an inverse model of a sensor associating with each distance measurement a vector of integers representing indices of probability classes associated with respective cells of an occupancy grid. 14. The system as claimed in claim 11, wherein said data processing module comprises a hardware block for integer computation, for so-called consolidation (F), configured to receive as input a plurality of integers representing indices of probability classes associated with cells of respective occupancy grids, and to compute an index of a probability class associated with a cell of said consolidated occupancy grid. 15. The system as claimed in claim 13, wherein said data processing module comprises a hardware block for integer computation, for so-called consolidation (F), configured to receive as input a plurality of integers representing indices of probability classes associated with cells of respective occupancy grids, and to compute an index of a probability class associated with a cell of said consolidated occupancy grid, wherein said data processing module also comprises at least one computation block, for so-called change of frame (R1, . . . , RNC), configured to: receive as input at least one first vector of integers, arising from a corresponding hardware block for computing occupancy probabilities and representing indices of probability classes associated with respective cells of an occupancy grid; convert it into a second vector of integers representing indices of probability classes associated with respective cells of another occupancy grid, coinciding spatially with said consolidated occupancy grid; and provide said second integer vector as input to said consolidation hardware block. 16. The system as claimed in claim 11, wherein said data processing module is configured to receive as input signals (z1, . . . , zNC) representative of distance measurements acquired successively by one and the same sensor or co-located sensors and to construct a consolidated occupancy grid on the basis of a plurality of these signals, corresponding to successive acquisition instants.