EP3060881A1

EP3060881A1 - Method for indoor and outdoor positioning and portable device implementing such a method

Info

Publication number: EP3060881A1
Application number: EP14783636.5A
Authority: EP
Inventors: Mehdi Boukallel; Alexandre PATAROT; Sylvie Lamy-Perbal
Original assignee: Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
Current assignee: Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
Priority date: 2013-10-24
Filing date: 2014-10-13
Publication date: 2016-08-31
Also published as: FR3012597A1; FR3012597B1; WO2015058986A1; US20160238395A1

Abstract

The invention relates to a device for positioning, suitable for being provided in a mobile body, which comprises at least one inertial measurement unit (1) including a 3D gyrometer and a 3D accelerometer, a calculation unit (2) which receives the measurement results from the gyrometer and from the accelerometer at a sampling rate, and restoration means (4, 5) for positioning the mobile body, the calculation unit performing: in a preliminary step (12) where said mobile body travels a known path, the calculation of a coefficient of proportionality between the distance that was actually traveled and the unprocessed distance obtained by integrating the time derivative of the acceleration along said path; in a subsequent free movement step (13), positioning said mobile body using the dead-reckoning navigation method, said positioning being carried out using, as the distance travelled, the unprocessed distance obtained by integrating the acceleration time derivative, corrected by the coefficient of proportionality. Moreover, a model representing the drift due to the bias in the angular speed measurements output by the gyrometer is calculated by a regression between said measurements and the orientation of said known path (A, B), the measurements taken into consideration in the free movement step (13) being said measurements corrected by subtraction of said model.

Description

METHOD FOR LOCATING INSIDE AND OUTSIDE AND PORTABLE DEVICE IMPLEMENTING SUCH A METHOD

The present invention relates to an indoor and outdoor location method, and a portable device implementing such a method. It is applicable for the location of pedestrians in indoor and outdoor environments. The invention is particularly applicable for first responders such as for example firefighters or police officers, isolated workers or people with visual or cognitive impairments.

Among all families of location systems, we distinguish first those depending on infrastructure or not. Thus, radio systems, such as GPS and Wifi in particular, require transmitters while barometers, magnetometers and inertial sensors operate autonomously. Among those that do not require infrastructure, there are those who depend on the environment, such as barometers for example, and those who do not depend on it, such as gyrometers providing only a mechanical measurement. The classification of these location systems can then be further refined by their integration ability and cost. For example, tactical inertial units integrated into aircraft or missiles are based on expensive but reliable optical gyrometers, while those embedded in mobile terminals are low cost but unreliable.

A technical problem to be solved is to locate a pedestrian in a constrained environment, for example in the floor of a building or in a dense urban environment, with low cost devices providing the only measures of acceleration and angular velocity, as well as the initial position of the user and a second known position either by this user or by an external system. Moreover, it must be possible to locate the mobile user with good accuracy, of the order of a few meters for example. For the internal location, a difficulty arises when it is necessary to ensure this location by eliminating infrastructure deployed in the environment such as beacons or antennas for example. To position the user, the location device must be equipped with measurement sensors. It estimates the position relative to a starting point and delivers the position information to the user himself or to other entities, such as display stations worn or remote, for example. Since the user is mobile, the device must be compact. A technical solution adopted must therefore minimize the size associated with the sensors.

Several localization solutions are known, in particular interior, without infrastructure.

Pedometer solutions rely on the measurement of the user's pace of operation. From the measurement of the number of steps and the step length, an estimate of the distance traveled is made. However, if the length of the user's pace changes, for example from a fast walk to a slow walk, the solution can lead to a poor estimate of the distance traveled and therefore the location of the user. Odometer solutions are widely used. The device then consists mainly of an inertial unit placed at the foot of the user. The acceleration and rotational speed measurements combined with estimated techniques make it possible to estimate the distance traveled by the user. A problem related to these solutions is the need for significant computing means to contain the significant drift of the inertial units. The location of the sensor on the stand also complicates the implementation.

In these solutions, the sensors are located at the waist, wrist or arm. The distance traveled is estimated from the acceleration measurement. However, these solutions often have reduced performance compared to the "foot-mounted" solution, particularly because of the location of these sensors on the body where the dynamics are less appropriate. They are not used alone but rather hybridized with other techniques to provide orientation information for example. Important calculation means are also necessary.

Other solutions use the measurement of the variation of the local terrestrial magnetic field to, for example, estimate the speed of advance of the user. Such a measurement can, on the one hand, require equipping the user with a compass, but the device is then sensitive to the presence of objects locally disturbing the earth's magnetic field in the user's environment, these objects being able to for example, furniture, metal structures or the electronic terminal itself. On the other hand, measurement may require to equip the user with a constellation of magnetic sensors to measure for example the spatial gradient of this magnetic field. But a disadvantage of this solution is the need to arrange the constellation of sensors precisely and to calibrate it carefully.

Finally, it is also known to use visual solutions. A camera equips the user and observes the field in front of the latter. This solution commonly combines two steps, that of estimating the advance of the mobile from points of the environment and the recognition of characteristic points. The first step makes it possible to estimate the distance traveled and the second step makes it possible to perform a registration of the position of the user relative to an absolute reference frame. A disadvantage of this solution is that it imposes to orient the camera in the moving field of the mobile. Moreover, because of the very principle of this technique, the camera is sensitive to lighting, orientation and vibration parameters. In addition, important calculation means are necessary to ensure the location of the mobile.

All these solutions do not meet the objectives of achieving a portable device reliable and compact, independent of the environment or any infrastructure, allowing in particular:

- A location with very small drifts, for example less than 5% of the total distance traveled, in a closed environment or not, without pre-equipment premises;

- An equipment with reduced dimensions and easily embarkable, adapted to be fixed to the size of a pedestrian for example;

- An economical calculation in resources but providing a robust estimate of the position of the user.

The object of the invention is in particular to achieve these objectives. For this purpose, the subject of the invention is a method as described by the claims.

The invention also relates to a device implementing such a method. Other characteristics and advantages of the invention will become apparent with the aid of the description which follows, given with regard to appended drawings which represent:

- Figure 1, the possible components of a device according to the invention and the steps they implement;

FIG. 2, the result of a correction of angular measurements by affine regression in real time;

FIG. 3 is a block diagram of an exemplary device according to the invention;

FIG. 4, an example of an algorithm implementing the method according to the invention.

Figure 1 shows the possible components of a device according to the invention and the steps they implement. By way of example, the invention is described for locating a pedestrian, but it can also be applied to the location of mobile bodies in motion.

The device comprises at least:

an inertial unit 1 capable of providing the accelerations of its center of inertia with respect to the terrestrial reference, expressed in its local coordinate system 3D, as well as the angular velocities expressed in this same local coordinate system; a calculation unit 2 capable of receiving digital data, for example in matrix form with tools of linear algebra, in real time, and of providing as quickly as possible the result of a calculation;

- A digital processing unit 3 capable of providing the data from the inertial unit 1 to the calculation unit 2 and retrieve the results to write them for example in a file or on a communication port. For example, a mobile terminal 4 completes the set. This terminal comprises an input interface 5 capable of supplying the digital processing unit with location information known a priori, either by the user or by an external system. For example, a button on the mobile terminal allows you to enter information in the system. The display means 6 of the terminal make it possible to restore estimated location information. The screen of the mobile terminal makes it possible, for example, to display either a point on a map or numerical values. Among the state-of-the-art solutions, some of which have been briefly described above, it is suggested to count steps by detecting them with accelerations and to multiply by an average length of one step for obtain a distance traveled, or, on the other hand, to integrate accelerations according to each dimension to obtain a distance traveled. In the latter case, due to measurement errors, the estimated value drifts over time. This is a first problem. A solution also proposed in the state of the art is then not to accumulate errors, especially when laying the foot on the ground when the station is stationary. This correction, however, requires placing the inertial unit at the level of the foot and then creates an equipment constraint, which is another problem. Finally, some solutions are based on stochastic readjustments such as particle filters, which often require significant computing resources to exploit market models or cartographic information, for example, leading to a third problem. A device according to the invention solves these three problems in particular while using an estimated navigation method also called "dead-reckoning". This method consists of estimating a 2D position from the distance traveled and projecting it according to the orientation (yaw) of the device over time. According to the invention, before engaging this method of "dead-reckoning", a preliminary phase of calibrations is performed.

The calibrations and the position calculation are carried out by the calculation unit 2. This unit implements three steps 1 1, 12, 13, the first two 1 1, 12 are two preliminary stages preceding the location by "dead-reckoning" Proper.

The first step 1 1, static, is performed while the wearer, so the device is immobile. The second step 12, dynamic, is performed on a known trajectory. Finally, the third step 13 performs the location of the carrier by "dead-reckoning" while it performs a free movement. The two preliminary steps 1 1, 12 make it possible to obtain coefficients correcting the distance and attitude calculations carried out during the third step 13 of locating the carrier in free movement, the distance and attitude calculations being used by the method from «dead reckoning ". As will be shown later in the description, these coefficients in particular correct the measurement bias of the sensors of the inertial unit. The inertial unit 1 is therefore composed of two sensors, a 3D accelerometer and a 3D gyrometer which respectively deliver accelerations and angular velocities. These sensors deliver, for example, measurements along three axes, the accelerometer delivering the accelerations (Ax, Ay, Az) and the gyrometer delivering the angular velocities (Wx, Wy, Wz) of the mechanical reference linked to the inertial unit. The measurements from these two sensors are biased by a random systematic measurement constant and are noisy by thermo-electronic effects for example. The invention deals with the influence of measurement biases without dealing with noises which can be annoying if they are treated by many known solutions.

The outputs of the gyrometers are biased. These biases can be estimated in the first preliminary step 1 1 while the wearer is motionless by calculating averages of measured angular velocities, while the angular velocity is ideally a zero vector. The calculated average gives an estimate of the bias.

However, this first estimate of bias is not sufficient in the majority of cases to accurately estimate the yaw drift estimated by integration of angular velocities. According to the invention, a calibration is then performed on a known trajectory, for example on a rectilinear portion between a point A and a point B when the lace is equal to the initial orientation, that is to say zero. The constant integral bias with respect to time, during the course of this portion A-B, creates a linear ramp whose coefficient can be estimated for example by affine regression and then subtracted from the estimated initial value. This calibration is performed during the second preliminary step 12.

FIG. 2 illustrates the result of the angular correction by affine regression in real time by two curves 21, 22 representing the angular value of the yaw with respect to time. A first curve 21 represents the lace without correction where the linear drift is observed with respect to time. A second curve 22 represents the yaw with bias correction.

FIG. 2 illustrates a particular case where the measurements of the gyrometer are corrected by subtraction of a ramp. This case may apply in particular when the known trajectory A-B is rectilinear. More generally according to the invention, the drift due to the gyrometer measurement bias is estimated by a model, for example linear regression estimating the coefficients of the line representing the linear drift as in the case of Figure 2. Then we subtract the model with orientation estimates (yaw) resulting from the raw measurements of angular velocities given by the gyrometer. It is possible to use another type of regression to calculate a drift model of the gyrometer measurements, especially when the known trajectory is not rectilinear. One can thus use a higher order polynomial regression or any other regression, the aim being to compare a model of evolution of the lace, resulting from the known trajectory portion and impacted by the bias, compared to the raw measurements.

Accelerometer measurements are also biased. In particular by the value of the gravity that is difficult to estimate according to each axis of the central 1 since its 3D orientation is unknown. To eliminate the constants, that is to say the bias and gravity, the invention uses a quantity called thereafter "jerk" which is the time derivative of the acceleration, corresponding to the third derivative of the distance by compared to time. The jerk is defined by a three-dimensional vector:

dA / dt = (dAx / dt, dAy / dt, dAz / dt), where A is the acceleration vector. We can also note J _k the jerk at a moment k, according to the following relation (1):

J _k = (A _k - A _k-1 ) / dt,

A _k and A _k- being the acceleration vectors at times k and k-1.

Subsequently, according to the invention, to avoid accumulating the errors attributable to the gyrometer during the change of markers of the acceleration vector, the standard jerk is advantageously used because it is invariant by change of reference. This change of reference corresponds here to the passage of the marker of the inertial unit attached to the carrier to the navigation mark attached to the Earth or the building in which moves the carrier. Advantageously, the use of the standard jerk avoids the projection errors on the reference the new marker.

The norm of the jerk is thus integrated with respect to the time, by three times, to obtain the accumulated distance traveled. However, the integrated value directly obtained does not correspond to the cumulative distance but to a proportional value. In order to obtain the coefficient of proportionality between the integrations of the jerk and the effective cumulative distance, it is necessary to know the distance traveled on a known portion of trajectory, for example on the portion A-B described above. Obtaining this coefficient is done in the second preliminary step 12 mentioned above.

Figure 3 shows a block diagram of a device according to the invention. The data provided by the inertial unit, that is to say the data of the accelerometer (Ax, Ay, Az) and the data of the gyrometer (Wx, Wy, Wz) are transmitted to the calculation unit 2. These data are for example transmitted at a frequency of the order of 100 Hz. The computing unit must be able to manage operations in real time, at least as fast as the arrival frequency of the inertial data, ie about 100 Hz.

The calculation unit implements the three steps 1 1, 12, 13 described above. External information 21 signals the calculation unit which step it must apply, in particular the calculation unit must know if the carrier is stationary for the first step 1 1, if it travels the known portion for the second step 12 or if he is on the move 13.

This outside information 21 can be activated in different ways. It can be provided by a button-type input device or an external location system, for example of the type RFID or GPS tag readers, provided that this location information is available. The information can therefore be given by the user, via a button or any interface, with for example the following indications on a screen "I am still" for the first step 1 1, "I walk between A and B" for the second step 12 and "locate me" for the third stage 13 in free movement. These situations can also be detected by external locating means which send the information to the computing unit. In the first step 1 1, while the carrier is stationary, the device calculates the average angular velocities from the inertial data. (Wx, Wy, Wz), to access the bias at the output of the gyrometers. Obtaining the biases along the three axes is done by comparing the average with the ideal case in which these outputs are zero. Indeed, in an ideal case without bias, and a stationary carrier, these inertial data are null. For example, the user remains immobile for 10 seconds in order to exploit a significant number of samples, 1000 if the sampling frequency is 100 Hz. At the end of this first step 11, an average vector Wmoy is accessed. three-dimensional containing the average angular velocities on each axis. The calculation of these average values is, for example, recursive during this first step. This bias estimate is stored in memory and used in the following steps for orientation calculation and angular corrections.

In the second step 12 the known trajectory of trajectory between a point A and a point B makes it possible to obtain on the one hand the coefficient of proportionality Γ between the cumulative distance obtained by integration of the jerk and the real distance traveled, and on the other hand share the coefficients a and β of the line representing the angular drift of the lace.

The coefficient of proportionality Γ is obtained between the integration of the jerk norm and the real distance, because at the end of the trajectory the distance between the points A and B is known. This factor is particularly given by the AB report _int / _REE AB AB n _e where _int is the integration of jerk and AB _SOE n _e is the known actual distance. As mentioned before, the integration of the jerk is a triple integration with respect to time, since the jerk is the time derivative of the acceleration given by the accelerometer of the inertial unit. It is in fact three integrations, according to the three axes, of the quantities of Ax / dt, dAy / dt and dAz / dt. Ax, Ay and Az are accelerations along the three axes. According to the invention, this temporal derivative of the acceleration vector is advantageously used because it makes it possible to eliminate the bias assumed to be constant. In addition, the norm of the vector is advantageously used because it is invariant, that is to say that it does not depend on the reference considered. This avoids the projection errors accelerations measured in the reference linked to the inertial unit to the navigation mark in which the carrier is located. Furthermore the known trajectory AB allows, if it is rectilinear for example, to know the orientation of the carrier which is the same orientation as the initial orientation. The points A and B may be signaled by the user or by an external system of the RFI D or GNSS type, for example, or any other location system. An example of identification of this known trajectory can be done using two terminals connected by a wire of known length. In order to carry out the second step 12, the first terminal, signaling the point A, is thus placed at one point and the wire is then stretched for example in a straight line to put the second terminal, signaling the point B. In a case where the wire measures 30 meters, there is thus between the two terminals A, B a rectilinear trajectory of 30 meters. The computing unit then receives the information that the user travels the path between A and B, either by the user himself via the aforementioned interface, or by means of external locations.

As shown in Figure 2, the lace is ideally zero on the rectilinear path portion. Figure 2 also shows that the angular velocity bias, assumed constant, derives the attitude linearly with respect to time. An affine regression then makes it possible to estimate the coefficients a and β of the line (y = a.t + β) representing this drift. This improves the estimation of the bias and corrects the yaw, that is, the attitude, over time.

To make the corrections, the attitude and the distance traveled must be estimated from this dynamic step 12. The attitude is calculated by integrating the angular velocities, via an integration of quaternions. Quaternions are a linear algebra tool that is easy to implement in a computing unit. They have the advantage of avoiding singularity problems, such as Euler angles and cardan lock in particular. That is, the calculation is efficient and fair regardless of the orientation, which is not the case with other methods. The distance is calculated by several integrations of the jerk norm and with the proportionality factor.

At this point, the system is not yet operational to navigate the estimate. It is only once the coefficients Γ, a and β are estimated that inertial gross measurements can be exploited 123 while minimizing the impact of biases. After the two preceding steps 1 1, 12, the system is operational to engage the third locating step 13, in free movement. The distance and attitude calculations are corrected by the coefficients resulting from the static calibration (immobility) and the dynamic calibration (rectilinear known trajectory) to provide a two-dimensional position, a speed and an orientation. This step is based on the well-known method of "dead reckoning".

In this step, the navigation is thus based on the technique of "dead reckoning" and is based on an attitude estimation by integrating quaternions and on a distance estimation by integration of the jerk norm. The device according to the invention uses the known method of "dead-reckoning" or navigation with the estimate but uses to carry out the localization according to this method the information of distance and yaw obtained respectively by the integration of the standard of the jerk, corrected for the coefficient of proportionality, and by the integration of angular velocities obtained via quaternions and corrected by linear regression.

FIG. 4 summarizes the possible operation of a device according to the invention and in particular the algorithm executed by the calculation unit 2. FIG. 4 illustrates the sequence of steps and the recursive calculations within each step . This algorithm implements the steps 1 1, 12, 13 previously described. The algorithm is looped between two samplings, for example at the frequency 100 Hz. The input data 41 at the instant k pass the various steps of the algorithm. The output data 42 completes the input data at the next instant k + 1.

The input data includes five groups of data:

- Calibration data (calculated during preliminary steps 1 1, 12);

- The measurement data provided by the sensors of the inertial unit, provided continuously, for all stages 1 1, 12, 13;

- User or external system entries indicating which stage the user is in;

- The estimates of the previous calculation loop;

- The constants. The input data at a time k used, in addition to those which precede, during the calibration steps are the following:

- the rough estimate of the angular velocity bias Wmoy, k (calculated during the first step 1 1);

the fine estimation of the angular velocity bias by the coefficients a, k and k (calculated during the second step 12);

- the coefficient of proportionality Γ between gross distance and actual distance (calculated during step 12)

The measurement data are on the one hand the acceleration vectors A, k-1 at time k-1 and the acceleration vector A, k at time k, the calculation of the jerk at time k being calculated from of these two values (see relation (1) above) and on the other hand the vector angular velocity W, k.

The user or system inputs are derived from the external information: immobile, known trajectory of the trajectory or free movement.

The previous estimates are the two-dimensional (2D) position R, k calculated in the free-movement phase 13, the attitude quaternion Q, k calculated from the second step 12 and the gross distance D, k on the portion of known trajectory also calculated from this second step. The gross distance is the cumulative distance calculated by integration of the jerk before correction by the coefficient of proportionality.

The constants are the effective distance AB _r _e n _e and the sampling frequency, 100 Hz for example.

The output data of an algorithm loop are the calibration data and the estimation data.

We can then follow the path of the algorithm of Figure 4 executed by the calculation unit 2.

As long as the outside information indicates that the carrier is in the stationary position (first step 13), the calculation unit 43 estimates the bias by calculating the mean angular velocity vector Wmoy constituting the rough estimation of the biases on the angular velocities, according to the three axes. The modified average value will be an input data at time k + 1. It is calculated by recursion, that is to say that:

Wmoy, k = (k / k + 1) Wmoy, k-1 + (1 / k + 1) W, k

Wmoy, k and Wmoy, k-1 being respectively the average vector calculated at times k and k-1 and W, k the measurement of the angular velocity at time k.

Recursion saves computing memory and is compatible with a real-time calculation executed between two successive samples. The average vector Wmoy obtained at the end of the step will be used in particular in the second step to integrate the attitude quaternion unbiased measurements.

As soon as the user enters the path of the known trajectory, that is to say that he is no longer stationary, the computing unit successively performs the calculation 44 of the attitude quaternion, the calculation 45 of the yaw angle by the previously described affine regression giving the orientation of the user, and the integration 46 of the jerk norm.

As long as the user is on the path of the known trajectory (second step 12), the calculation unit 47 estimates the proportionality factor Γ which becomes an output datum. This coefficient is calculated recursively, it may be obtained as follows: r k = r k-1 + 5d, k / AB _reene

r, k and r, k-1 being the coefficient of proportionality respectively at times k and k-1.

5d, k is the integration of the jerk Jk between the instants k-1 and k, that is to say the gross distance traveled, that is:

5d, k = (dt ^3/3) Jk || || (2)

dt is the inverse of the sampling frequency F (dt = 1 / F), and corresponds to the time interval between the instants k-1 and k.

As soon as the user has finished traversing the known trajectory and initiates the free movement 13, the calculation unit 48 integrates the jerk in the same way as in the second 12, according to the relation (2) above. The actual distance 5d, n _e k _SOE traveled between two sampling instants k-1 and k is calculated by multiplying this integration by the coefficient Γ obtained at the end of the second step 12.

The actual distance traveled is obtained by recursion, the actual distance d, k + 1 traveled at time k + 1 being obtained from the actual distance d, k traveled at time k in the following manner: d, k = d, k + 1 + T x 5d, k

From the actual calculated distance, the computing unit determines the location of the user according to the "dead reckoning" method.

The algorithm of FIG. 4 is a possible mode of implementation of the steps 1 1, 12, 13. Other modes of implementation are possible. In particular in the first step 1 1, it is possible to calculate the angular velocity Wmoy by a non-recursive method. It is the same in the second step 12 for the calculations of the coefficient Γ to deduce the actual distance from the integration of the norm of the jerk, and the a and β for calculating the angle of the lace. Optionally the first step 1 1 may not be implemented according to the accuracy that is desired calculation of the attitude. Bias offsets solve the problem of time drift. In addition, these compensations do not in any case involve step detection, which makes it possible to overcome the assumptions of laying the foot on the ground and therefore the periodic immobility of the plant. Thus, the central can be placed at the level of the belt, and even at another location of the body of the user.

Finally, the invention is compatible with the constraints of real time and moderate use of the memory thanks to the recursion because each state (position, attitude, coefficient of proportionality Γ between the norm of the jerk and the distance, coefficients of regression affine a and β) can be estimated from its value at the previous instant. In addition, only the raw inertial data, without pseudo-measurement of the step detection type, are exploited, which alleviates the calculation.

A device according to the invention is therefore easy to wear. It comprises an inertial unit, calculation means and display means, a screen for example, in particular for restoring the position by displaying a point on a map. It may also include means for transmitting the location data on a remote server.

The choice of a gyrometer is particularly appropriate because the orientation estimates do not depend on the external environment as in the case of magnetometers used in systems according to the prior art.

One can add a map of the places where the user moves, which can improve its guidance from the location data. To reduce the impact of white noise on the measurements, it is possible to use a Kalman filter for example.

The method implemented by the invention, in particular the algorithm executed by the calculation unit 2, enables real-time localization with reduced computing resources with a result similar to that of the devices of the state of the system. art in terms of accuracy or better. The necessary equipment is low cost and the sensors used, gyrometer and accelerometer, can be miniature. This material is also easily integrated into already commercialized technologies, for example mobile phones. The equipment can be quickly installed on the user. It is compact and can equip all types of pedestrians. The device can operate alone or be hybridized with another location system.

In one embodiment with a mobile terminal 4 containing the input interface 4 and the display 6, this terminal may advantageously be a smartphone-type mobile terminal. In this case, the computing unit 2 can be connected to a communication port enabling it to communicate with the mobile terminal via a wireless link, of the Wifi or Bluetooth type, possibly by wired connection. Advantageously, the wireless communication can be done according to the standard protocol http, allowing the interface with all types of mobile terminals with a web browser. The user can then enter his instructions on the terminal and read the display of location results on the same terminal.

The invention has been described for locating a pedestrian. It can also apply to locate a person traveling in a wheelchair. More generally, it can be applied to locate moving bodies such as wheeled or crawler vehicles, wagons, or land or air drones.

Claims

1. A method of locating a moving body, characterized in that said movable body being equipped with at least one inertial unit (1) comprising a 3D gyrometer and a 3D accelerometer, said method comprises:

a preliminary step (12) in which, said moving body traversing a known trajectory (A, B), a proportionality coefficient (Γ) is calculated between the distance actually traveled and the gross distance obtained by the integration of the norm. the time derivative of the acceleration (Jk) on said trajectory;

a next step (13) of free displacement in which the location of said mobile body is carried out by the estimated navigation method, the location being carried out using the distance traveled as the gross distance obtained by integrating the standard. of the time derivative of the acceleration corrected by the coefficient of proportionality (Γ).

2. Method according to claim 1, characterized in that in the preliminary step (12), a model representing the drift due to angular velocity measurements delivered by the gyrometer is calculated by a regression between said measurements and the orientation. on said known trajectory (A, B), the measurements taken into account in the step (13) of free displacement being said measures corrected by subtraction of said model.

3. Method according to claim 2, characterized in that, the trajectory (A, B) being rectilinear, said model is obtained by linear regression between said measurements and the orientation said known trajectory.

4. Method for locating a mobile body, characterized in that said mobile body being equipped with at least one inertial unit (1) comprising a 3D gyrometer and a 3D accelerometer, said method comprises:

a preliminary step (12) in which, said moving body traversing a known trajectory (A, B), a model representing drift due to angular velocity measurements is calculated delivered by the gyrometer by a regression between said measurements and the orientation of said known trajectory (A, B);

a next step (13) of free displacement in which the location of said movable body is performed by the estimated navigation method, the location being performed using said measurements, corrected by subtracting said model.

5. Method according to claim 4, characterized in that, the trajectory (A, B) being rectilinear, said model is obtained by linear regression between said measurements and said trajectory.

6. Method according to any one of claims 4 or 5, characterized in that in the preliminary step (12) is calculated a coefficient of proportionality (Γ) between the actual distance actually traveled and the gross distance obtained by the integration. the time derivative derivative of the acceleration (Jk) on said trajectory, the location being effected in the step (13) of free displacement using the distance traveled by the gross distance obtained by the integration of the norm of the temporal derivative of the acceleration corrected by the coefficient of proportionality (Γ).

7. Method according to any one of the preceding claims, characterized in that it comprises a first preliminary step (1 1) wherein, said movable body being stationary, the average values of the angular velocity measured along the three axes are calculated. of the gyrometer, said average values forming a first estimate of the gyrometer measurement bias.

8. Locating device adapted to equip a mobile body, characterized in that said device comprises at least one inertial unit (1) comprising a 3D gyrometer and a 3D accelerometer, a calculation unit (2) receiving the results of measurements of the gyrometer and the accelerometer according to a sampling frequency, and means for restitution (4, 6) of location of said moving body, the computing unit performing:

in a preliminary step where said moving body travels a known trajectory, calculating a coefficient of proportionality (Γ) between the distance actually traveled and the gross distance obtained by the integration of the time derivative of the acceleration along said trajectory;

in a next step (13) of free displacement, locating said moving body by the estimated navigation method, the location being carried out using as distance traveled the gross distance obtained by the integration of the time derivative of the acceleration corrected by the coefficient of proportionality (Γ).

9. Device according to claim 8, characterized in that in the preliminary step (12), the calculation unit (2) calculates a model representing the drift due to measurements delivered by the gyrometer by a regression between said measurements. and the orientation of said known trajectory (A, B), the measurements taken into account in the step (13) of free displacement being said measures corrected by subtraction of said model.

10. Device according to claim 9, characterized in that, the trajectory (A, B) being rectilinear, said model is obtained by linear regression between said measurements and said trajectory.

1 1. Locating device adapted to equip a mobile body, characterized in that said device comprises at least one inertial unit (1) comprising a 3D gyrometer and a 3D accelerometer, a calculation unit (2) receiving the results of measurements of the gyrometer and the accelerometer according to a sampling frequency, and means for restitution (4, 6) of location of said moving body, the computing unit performing:

in a preliminary step where said moving body travels a known trajectory, calculating a model representing the drift due to angular velocity measurements delivered by the gyrometer by a regression between said measurements and the orientation of said known trajectory (A, B);

in a next step (13) of free displacement, locating said moving body by the estimated navigation method, the location being performed using said measurements, corrected by subtracting said model.

12. Device according to claim 1 1, characterized in that, said trajectory (A, B) being rectilinear, said model is obtained by linear regression between said measurements and said trajectory.

13. Device according to any one of claims 1 1 or 12, characterized in that in the preliminary step (12), the calculation unit (2) performs the calculation of a coefficient of proportionality (Γ) between the distance actually traveled known and the gross distance obtained by the integration of the standard of the time derivative of the acceleration (Jk) on said trajectory, the location being carried out in the step (13) of free displacement using as distance traveled the gross distance obtained by integrating the standard of the temporal derivative of the acceleration corrected by the coefficient of proportionality (Γ).

14. Device according to any one of claims 8 to 13, characterized in that it performs a first preliminary step (1 1) wherein, said movable body being stationary, the calculation unit (2) calculates the average values the angular velocity measured along the three axes of the gyrometer, said average values forming a first estimate of the gyrometer measurement bias.

15. Device according to any one of claims 8 to 14, characterized in that the computing unit (2) estimates the attitude by integration of quaternions, said attitude being used by the method of navigation to the estimated.

Device according to any one of claims 8 to 15, characterized in that the computing unit (2) communicates with an input interface (5) receiving an external signal informing the computing unit (2) of turn on a next step (12, 13).

17. Device according to claim 16, characterized in that said external signal is activated by said movable body.

18. Device according to claim 16, characterized in that said external signal is activated by an external location system.

19. Device according to any one of claims 16 to 18, characterized in that, the gyrometer and the accelerometer delivering the measurement results at a given sampling frequency, the calculation unit (2) performs the first step preliminary step (1 1), the step of traveling on a known trajectory (12) and the free moving step (13) recursively at the rate of said sampling frequency, said steps being triggered successively by said external signal .

20. Device according to claim 1 1, characterized in that as the external signal indicates that said movable body is stationary (13) the calculation unit (2) makes the estimation (43) bias by calculating the vector mean angular velocity constituting the estimate of the angular velocity bias, along the three axes, the average value being calculated by recursion, so that:

Wmoy, k = (k / k + 1) Wmoy, k-1 + (1 / k + 1) W, k

Wmoy, k and Wmoy, k-1 being respectively the average vector calculated at the sampling instants k and k-1 and W, where k is the measure of the angular velocity at time k.

21. Device according to any one of claims 19 or 20 and 8 or 13, characterized in that as long as the external signal indicates that said moving body is following the path of the known trajectory, the calculation unit performs (47) the estimation the proportionality factor Γ, this coefficient being calculated recursively, so that:

r, k = r k-1 + 5d, k / AB _reene

r, k and r, k-1 being the coefficient of proportionality respectively at the sampling instants k and k-1 and 5d, k is the integration of the standard of the time derivative of the acceleration between the instants k-1 and k.

22. Device according to any one of claims 19 to 21 and 8 or 13, characterized in that in the step (13) of free movement, the actual distance traveled is obtained by recursion, the actual distance d, k + 1 traveled at the time of sampling k + 1 is obtained from the actual distance d, k traveled at the sampling instant k in the following manner: d, k = d, k + 1 + T x 5d, k

where Γ is the coefficient of proportionality calculated in the step (12) of travel on a known trajectory and 5d, k is the integration of the standard of the time derivative of the acceleration between the instants k-1 and k.

Device according to claims 8 to 22, characterized in that a mobile terminal (4) comprises the reproduction means (6) and the input interface (5), said terminal communicating with the calculation unit via a wireless link according to an exchange protocol based on the standard http protocol.

24. Device according to any one of claims 8 to 23, characterized in that it is adapted to be worn at the size of a person.