Method for characterizing the environment of a mobile device, producing a static space grid and/or a free space grid

A method for characterizing the environment of a mobile device, wherein, for each iteration at a time t, the following steps are implemented:

• S10) Acquiring a plurality of distance measurements (zt) in the environment by way of at least one sensor;
• S20) Generating a pair (wt) of occupancy grids at the time t−1(OGt-1) and at the time t(OGt), each grid (OGt-1, OGt) fusing the distance measurements into a discretized spatial representation of the environment;
• S30) Generating a static space grid at the time (SGt), each cell (SGt(i)) of the static space grid at the time t having a subsequent probability of said cell being occupied by a static tangible body at the time t (P(st|w1:t)), said subsequent probability P(st|w1:t) being computed for each cell (SGt(i) by way of a binary Bayesian filter from:
• the subsequent probability of said cell being occupied by a static tangible body at the time −1 (P(st-1|w1:t−1)), said subsequent probability of said cell being occupied by a static tangible body at the time t−1 (P(st-1|w1:t−1)) being injected directly at input of the binary Bayesian filter;
• what is referred to as a static prediction model corresponding to a probability of the cell being occupied by a static tangible body at the time t as a function of the state of the cell at the time t−1 (P(st|st-1));
• and
• what is referred to as a static inverse model at the time t(P(st|wt)) corresponding to a probability of knowing that the cell is occupied by a static tangible body at the time t from the pair of occupancy grids at the time t−1 and at the time t;
• and/or
• S40) Generating a free space grid at the time t (FGt), each cell (FGt(i)) of the free space grid at the time t having a subsequent probability of said cell being free at the time t P(ft|w1:t)), said subsequent probability being computed for each cell by way of a binary Bayesian filter from:
• the subsequent probability of said cell being free at the time t−1 (P(ft-1|w1:t−1)), said subsequent probability of said cell being free at the time t−1 (P(ft-1|w1:t−1)) being injected directly at input of the binary Bayesian filter;
• what is referred to as a free prediction model corresponding to a probability of the cell being free at the time t as a function of the state of the cell at the time t−1 (P(ft|ft-1));
• and
• what is referred to as a free inverse model at the time t (P(ft(wt)) corresponding to a probability of knowing that the cell is free at the time t from the pair of occupancy grids at the time t−1 and at the time t.