Abstract: The present invention relates to a navigation assistance method for a mobile carrier (1) comprising an inertial navigation unit (10) comprising at least one inertial sensor (12), in which, over a determined observation window, the following steps are implemented by an estimation unit (11) of the inertial navigation unit (10): (E10) parametrisation of a non-linear system configured to estimate a navigation state of the mobile carrier (1) over a given time interval at an iteration n as a function of a kinematic model and/or measurements acquired by at least one inertial sensor (12); (E20) linearisation of the system so that the system expresses a navigation state at iteration n as a function of the state at iteration n?1 and a correction to this navigation state, said system being initialised by a first a priori state; (E21) estimating, by a Kalman filter and stochastic cloning, a first correction of a navigation state at iteration n; (E22) estimating a second correction of the navigation state at iteration n b

Abstract: The invention relates to an alignment method based on a simplified mode allowing processing using an invariant Kalman filter, in which each speed involved in the navigation equations is expressed in a work reference frame (Rt) translated with respect to an inertial reference frame (Ri) and for which the origin moves along a reference inertial trajectory, the carrier of which is assumed to be close (geographic origin for alignment with the sun at known position, GPS trajectory for alignment in-motion, etc.). This simplified mode comprises the repetition of the following steps to estimate a mobile carrier state (P): —propagation (PROP) determining an estimated current state from a preceding estimated state, inertial sensor measurements and theoretical information on the carrier trajectory (P) —updating (MAJ) the estimated state using theoretical information on the carrier trajectory (P). The deterministic uncertainties of the sensors (bias/drift/scale factors, etc.

Abstract: A method for estimating the state of a moving system, using a particle filter. The method implements on one reference particle an estimation of the state and includes a first set of variables and a second set of variables. A random mutation, by the particle filter, of the first set of variables is performed followed by parameterization of an extended Kalman filter using the mutated first set of variables. The Kalman filter produces a new particle using the second set of variables and data measured by at least one sensor. The second set of variables includes at least the variables: orientation, speed and position of the moving system. The Kalman filter is configured assuming that the pair of variables orientation and position has a property of invariance upon a rotation or translation and their orientation and speed has a property of invariance on application of a rotation or translation.

Abstract: A method for tracking the navigation of a mobile carrier, in which an extended Kalman filter estimates, over successive iterations, a navigation state of the carrier. Each iteration propagates a previous navigation state of the carrier into a propagated state, updates the propagated state according to measurements acquired by at least one navigation sensor. The updating includes: calculating a linear correction term from an innovation representative of a difference between the measurements acquired by the navigation sensor and the propagated state, calculating an exponential of the linear correction in terms of a Lie group, calculating a first correction term expressed in a reference frame that is fixed relative to the carrier, calculating a second correction term expressed in an inertial reference frame in which the carrier is mobile, and adding the second correction term to the value of the variable contained in the propagated state.

Abstract: The invention proposes a method for tracking the navigation of a mobile carrier, in which an extended Kalman filter estimates, over successive iterations, a navigation state of the carrier, one iteration of the filter comprising steps of: propagating a previous navigation state of the carrier into a propagated state according to a kinematic model and/or measurements acquired by at least one inertial sensor, updating the propagated state according to measurements acquired by at least one navigation sensor, in which, for at least one navigation variable of the carrier contained in the propagated state, the updating comprises sub-steps of: calculating a linear correction term from an innovation representative of a difference between the measurements acquired by the navigation sensor and the propagated state, calculating an exponential of the linear correction in terms of a Lie group, calculating a first correction term expressed in a reference frame that is fixed relative to the carrier, the first correction ter

Abstract: A method for controlling displacement of a load suspended to a point of attachment of a lifting machine includes an acquisition step during which a piloting setpoint is acquired and which is representative of the displacement speed that the operator wishes to confer on the suspended load, a processing step during which a setpoint called execution setpoint, which is applied to a drive motor in order to displace the suspended load, is elaborated from the piloting setpoint, the processing step including a C3 smoothing substep by third-order filtering during which a third-order filter is applied to the piloting setpoint in order to generate a filtered piloting setpoint of smoothness class C3, and the execution setpoint is defined from the filtered piloting setpoint.

Abstract: A method is provided for estimating the state of a moving system, the method using a particle filter and comprising the following steps implemented on one reference particle among a plurality of previously calculated particles, the reference particle providing an estimation of the state and comprising a first set of variables and a second set of variables: random mutation (101), by the particle filter, of the first set of variables into a mutated first set of variables; parameterization (102) of an extended Kalman filter using the mutated first set of variables; and implementation (103) of the parameterized extended Kalman filter to produce a new particle by way of the second set of variables and data measured by at least one sensor, the method being characterized in that the extended Kalman filter is an invariant extended Kalman filter, and in that the second set of variables comprises at least the variables: orientation, speed and position of the moving system, the invariant extended Kalman filter being con

Abstract: The invention relates to an alignment method based on a simplified mode allowing processing using an invariant Kalman filter, in which each speed involved in the navigation equations is expressed in a work reference frame (Rt) translated with respect to an inertial reference frame (Ri) and for which the origin moves along a reference inertial trajectory, the carrier of which is assumed to be close (geographic origin for alignment with the sun at known position, GPS trajectory for alignment in-motion, etc.). This simplified mode comprises the repetition of the following steps to estimate a mobile carrier state (P): propagation (PROP) determining an estimated current state from a preceding estimated state, inertial sensor measurements and theoretical information on the carrier trajectory (P) updating (MAJ) the estimated state using theoretical information on the carrier trajectory (P). The deterministic uncertainties of the sensors (bias/drift/scale factors, etc.

Abstract: A method for estimating the angular deviation of a moving element relative to a reference direction comprises the following steps: acquisition of a reference image of a reference direction of the moving element; acquisition of a current image of the current direction of the moving element; identification of points of interest in the reference image and in the current image; determination of at least two pairs of points of interest, one pair being made up of a point of interest in the current image and of a point of interest corresponding thereto in the reference image; determination of the angular deviation between the current direction and the reference direction of the moving element by using the at least two pairs of points identified in the preceding step.