US 12,227,191 B2
Iterative method for estimating the movement of a material body by generating a filtered movement grid
Tiana Rakotovao Andriamahefa, Grenoble (FR)
Assigned to COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES, Paris (FR)
Filed by COMMISSARIAT A L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES, Paris (FR)
Filed on Nov. 19, 2021, as Appl. No. 17/531,483.
Claims priority of application No. 2011937 (FR), filed on Nov. 20, 2020.
Prior Publication US 2022/0185294 A1, Jun. 16, 2022
Int. Cl. B60W 40/08 (2012.01); B60W 60/00 (2020.01); G06F 18/2415 (2023.01)
CPC B60W 40/08 (2013.01) [B60W 60/001 (2020.02); G06F 18/2415 (2023.01); B60W 2040/0881 (2013.01); B60W 2420/403 (2013.01)] 17 Claims
OG exemplary drawing
 
1. An iterative method, implemented by a computer or by a dedicated electronic circuit, for estimating a movement of at least one material body in a surrounding space of an autonomous device, said surrounding space being discretized into a grid of cells, wherein, for each given iteration t, the following steps are implemented:
a) obtaining an inconsistency grid in iteration t, generated in response to detection of a change of occupancy state in at least one of the cells of the grid of cells between an iteration t-1 and iteration t, each cell of the grid of cells and each cell of the inconsistency grid having a binary value,
b) recurrently generating a filtered movement grid in iteration t comprising, for each cell of the grid of cells, a posterior probability of the material body having performed a movement with at least one movement component characterizing said movement, said movement component forming part of a discrete set of movement components, said filtered movement grid in iteration t being generated using the filtered movement grid in iteration t-1, an inconsistency grid from iteration t-1, and the inconsistency grid in iteration t,
wherein step b) comprises applying a binary Bayesian filter, each movement component of the discrete set of components being an independent binary random variable estimated by said binary Bayesian filter, the posterior probability corresponding to an output of said binary Bayesian filter, and
wherein navigation of said autonomous device being controlled based on said filtered movement grid.