Method for attitude readjustment by a dead reckoning system using a relative positioning system

Abstract

The present invention relates to an attitude readjustment method (107) including: a step (116) of estimating an estimated position of a dead reckoning system (22) in an arbitrary fixed coordinate system at a plurality of determination times by the dead reckoning system (22); a step (114) of obtaining an evaluated position of the dead reckoning system (22) evaluated by a relative positioning system (24) in a predetermined fixed coordinate system at each determination time; and a step (124) of deriving at least one attitude readjustment parameter by minimizing a cost function for comparing the evaluated position and the estimated position corrected by each readjustment parameter.

Description

Technical Field

[0001] The present invention relates to dead reckoning technology, in particular to the attitude readjustment technology provided by a dead reckoning system. The present invention is advantageously applicable to urban or indoor environments, i.e., movement within buildings.

Background Art

[0002] Currently, it has become common to track the position of an object by multi-lateration by measuring the distance between a receiver attached to the object and at least three reference points whose positions in the environment are known. This utilizes a positioning system such as GNSS (Global Navigation Satellite System, e.g., GPS), or the infrastructure of a wireless communication network (e.g., Wi-Fi network, GSM network, etc.). However, these methods are very limited because the masking that can occur between the reference points and the receiver makes the availability and accuracy of the information not guaranteed. Therefore, for use in urban and indoor environments, the introduction of a high-cost infrastructure with a large number of reference points distributed throughout the environment is required. Also, since these rely on external technologies such as GNSS satellites, they may be unavailable or intentionally interfered with.

[0003] Alternatively, there is also known a method of tracking the relative position of an object in any environment by a motion sensor that measures the movement of the object, called dead reckoning. The relative position means the position of the object in space with respect to a point and a coordinate system given at initialization. In these methods, in addition to the position, the orientation of the object (also called "attitude") with respect to the same initial coordinate system, given by Euler angles (roll φ, pitch θ, yaw ψ) in three dimensions and heading ψ in two dimensions, can be obtained. These methods are suitable for movement in environments where position tracking by multi-lateration is difficult, such as urban and indoor environments.

[0004] There are various types of dead reckoning. The most common one is what is called "simple" inertial navigation, which is implemented in heavy-duty applications such as the navigation of fighter jets, airliners, submarines, and ships. This generally relies on an inertial unit composed of at least three accelerometers and three gyroscopes arranged on three axes. Generally, the gyroscopes "maintain" the coordinate system, and in this coordinate system, by double time-integrating the measurement values of the accelerometers, the movement can be estimated. It is well known that very high-precision sensors are required to use this "simple" inertial navigation. In fact, the double time-integration of acceleration measurements means that a position error is generated where a certain acceleration error increases in proportion to the square of time.

[0005] As another dead reckoning technique, a technique is known that provides velocity vector information in the coordinate system of an object from an external source (for example, an odometer of a car, a log of a boat, a pitot tube of an airplane). By applying simple integration to this velocity vector information and combining it with the attitude of the object, especially the azimuth information, its trajectory can be known. If the measurement error of the sensor is the same, the influence of time drift is reduced.

[0006] In most cases, the initial attitude is known, for example, by the initial "alignment" of the inertial device or by a sensor other than the inertial sensor (such as a magnetic sensor). However, due to the measurement error of the inertial sensor, a time drift occurs in the measured attitude, which invalidates the information of the initial attitude after a certain period of time depending on the accuracy of the sensor used, and the position of the object becomes inaccurate. For example, if there is a 1% error in azimuth measurement, a 1m error will occur in the position of the object after moving 100m.

[0007] To address this, it is known to periodically readjust the measurements of the dead reckoning system by another positioning system so as to best maintain the attitude information of the object, especially the azimuth information. For example, from International Publication No. WO 2019 / 020961, it is known to readjust the azimuth information using the magnetic azimuth measurement values obtained by a magnetometer mounted on the object.

[0008] However, this solution is not entirely satisfactory. In fact, due to its own error and drift problems, the magnetometer may not be reliable enough to be used as the basis for readjustment. Also, in this solution, it is necessary to incorporate a magnetometer into the object whose position is to be tracked, increasing the cost. SUMMARY OF THE INVENTION

[0009] One object of the present invention is to propose a simple and economical solution for readjusting the attitude provided by an inertial navigation system. Another object is to enable such readjustment to be performed with high accuracy over short distances. Another object is to enable accurate tracking of people moving within a building in a simple and economical way.

[0010] For this purpose, according to a first aspect, the present invention is an attitude readjustment method for readjusting the attitude provided by an inertial navigation system, comprising: estimating, by an inertial navigation system, the estimated position of the inertial navigation system in an arbitrary fixed coordinate system at each of a plurality of decision times included in a moving section in which the inertial navigation system moves along a movement trajectory; for each decision time, obtaining the evaluated position of the inertial navigation system in a predetermined fixed coordinate system at the decision time, evaluated by a relative positioning system; deriving at least one attitude readjustment parameter by minimizing a cost function that compares the evaluated position with the estimated position corrected by each readjustment parameter.

[0011] According to a specific embodiment of the present invention, the attitude readjustment method has one or more of the following features, alone or in any technically possible combination.

[0012] The inertial navigation system is worn by a pedestrian, and preferably, the inertial navigation system is worn on the foot or ankle of the pedestrian.

[0013] The dead reckoning system includes a motion sensor for measuring the movement of the dead reckoning system and a processing unit for deriving the attitude and position of the dead reckoning system from the measured movement.

[0014] The relative positioning system is selected from a triangulation system, a trilateration system, a map matching system, and a visual positioning system, and preferably, the relative positioning system includes an ultra-wideband telemetry device.

[0015] Any fixed coordinate system and a predetermined fixed coordinate system have a common axis, and the attitude readjustment parameter is a parameter for correcting the attitude by rotation about the common axis, preferably composed of an angle.

[0016] The common axis is the vertical axis.

[0017] The cost function represents the average geometric deviation between the evaluation position and the estimated position after applying a geometric transformation including rotation and preferably translation to the evaluation position or the estimated position.

[0018] The rotation is performed about the rotation axis, and the translation is performed in a direction orthogonal to the rotation axis.

[0019] The step of deriving the readjustment parameter includes calculating a candidate value of the readjustment parameter, evaluating an accuracy value of the candidate value, comparing the accuracy value with a previous accuracy value related to the previous readjustment parameter, and determining the readjustment parameter according to the result of the comparison, and the readjustment parameter depends on the candidate value and the previous readjustment parameter.

[0020] The accuracy value is a function of the uncertainty of the estimated position and the evaluation position, and / or a function of the average geometric deviation between the evaluation position and the estimated position after applying the candidate value of the readjustment parameter.

[0021] The step of deriving the readjustment parameter is a) calculating a first candidate value of the readjustment parameter by minimizing a cost function that compares an evaluation position for N decision times with an estimated position for the N decision times corrected by the readjustment parameter; b) calculating a first accuracy value associated with the first candidate value; c) calculating a second candidate value of the readjustment parameter by minimizing a cost function that compares an evaluation position for N - 1 decision times obtained by subtracting the oldest decision time from the N decision times with an estimated position for the N - 1 decision times corrected by the readjustment parameter; d) calculating a second accuracy value associated with the second candidate value; e) comparing the first accuracy value with the second accuracy value; f) selecting the first candidate value if the first accuracy value reflects the best accuracy.

[0022] If the accuracy value reflecting the best accuracy is constituted by the second accuracy value, the step of deriving the readjustment parameter includes deleting the evaluation position and the estimated position for the oldest decision time; reducing N by 1 and repeating sub - steps a) to e).

[0023] The dead - reckoning system has an accuracy on the order of a few percent, for example, 1 - 3% with respect to the moving distance, and an accuracy on the order of several tens of degrees per hour, for example, 30 - 80 degrees per hour with respect to the azimuth drift. The relative positioning system has an accuracy on the order of several tens of centimeters, for example, 20 cm - 1 m with respect to the position.

[0024] Also, according to a second aspect of the present invention, there is provided a method for specifying the position of an object equipped with a dead - reckoning system in a predefined space, the method including activating the dead - reckoning system; A step of readjusting the attitude of a dead reckoning system by using a relative positioning system including infrastructure installed at an access point to a predefined space so as to obtain attitude readjustment parameters, the step of readjusting the attitude by implementing the attitude readjustment method described in any of the above, A step of readjusting the position of a dead reckoning system by using a relative positioning system including infrastructure installed at an access point to a predefined space so as to obtain position readjustment parameters, A step of calculating, by the dead reckoning system, a calculated position of the dead reckoning system in a predetermined coordinate system by using the attitude readjustment parameters and the position readjustment parameters. The present invention relates to a position identification method including these steps.

[0025] According to a specific embodiment of the present invention, the position identification method also has the following features.

[0026] The infrastructure is mounted on the vehicle itself equipped with a position and attitude system capable of calculating the position of the infrastructure in a predetermined coordinate system.

[0027] Further, according to a third aspect of the present invention, there is provided a dead reckoning system including a motion sensor for measuring the movement of the dead reckoning system, and a processing unit for deriving the attitude and position of the dead reckoning system in a fixed coordinate system from the measured movement. The processing unit is configured to execute the attitude readjustment method according to the first aspect in order to readjust the attitude. The present invention relates to such a dead reckoning system.

[0028] According to a fourth aspect of the present invention, there is provided a computer program product including code instructions for executing the attitude readjustment method according to the first aspect when the program is executed by a processor.

[0029] Finally, according to a fifth aspect of the present invention, there is provided a computer-readable storage means recorded with a computer program product including code instructions for executing the attitude readjustment method according to the first aspect.

[0030] Other features and advantages of the present invention are given for illustrative purposes only and will become apparent from the following description with reference to the accompanying drawings.

Brief Description of the Drawings

[0031]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Best Mode for Carrying Out the Invention

[0032] The position identification system 10 shown in FIG. 1 is for identifying the position of the object 12 within the predefined space 14. For this purpose, the position identification system 10 includes a plurality of position identification boxes 16 respectively attached to the object 12. Further, the position identification system 10 includes a position identification infrastructure 18 disposed at at least one access point 19 to the predefined space 14.

[0033] Referring to FIG. 2, each object 12 is here a pedestrian. The present invention is particularly advantageous in such applications because the box 16 takes up little space and can be easily worn ergonomically by the pedestrian. As a variant (not shown), the object 12 can be any moving object that requires position information, such as a wheeled vehicle or a drone, for example.

[0034] The predefined space 14 is typically the interior of a building. For example, in an industrial site, the pedestrian 12 is a technician working at the industrial site. Alternatively, when the predefined space 14 is an intervention site such as a fire site or a hostage-taking site, the pedestrian 12 is a firefighter or a soldier intervening at the site.

[0035] Each positioning box 16 is typically worn on the limbs of the pedestrian 12, here the legs, preferably the feet or ankles. For this purpose, each positioning box 16 comprises a fixture 20, as seen in FIG. 3, consisting of a wristband that surrounds the limb and enables a fixed connection, for example a hook-and-loop fastener. As a variant (not shown), the fixture 20 can be constituted by any element capable of fixedly connecting the positioning box 16 to the object 12.

[0036] Referring further to FIG. 3, the positioning box 12 comprises a dead reckoning system 22 and a relative positioning system 24. In the illustrated example, the positioning box 12 also comprises a communication system 26, typically a wireless communication system, for communication with an external device such as a portable terminal 29 (FIG. 2), for example a multifunctional portable terminal, or a remote server (not shown). Optionally, a storage module 28 is also provided.

[0037] The dead reckoning system 22 comprises a motion sensor 30 for measuring the movement of the dead reckoning system 22 and a processing unit 32 for deriving the attitude and position of the dead reckoning system 22 in a predefined fixed coordinate system, for example an east-north-up (ENU) coordinate system, from the measured movement. The "fixed coordinate system" means a coordinate system fixed in a ground reference system.

[0038] In the example described herein, the dead reckoning system 22 is configured to operate in a two-dimensional coordinate system and is configured to provide only the orientation of the dead reckoning system 22 in the horizontal plane of a given coordinate system and two position coordinates. As a variant (not shown), the dead reckoning system 22 may be configured to operate in a three-dimensional coordinate system and to provide the roll angle, pitch angle, and yaw angle of the dead reckoning system 22 in a given coordinate system and its three position coordinates. A person skilled in the art would be able to easily replace the example shown for the two-dimensional case with the three-dimensional case.

[0039] The motion sensor 30 includes a gyroscope 40 that measures the angular velocity of the dead reckoning system 22 according to a system having three orthogonal axes that define a moving coordinate system fixed to the box 16, that is, measures the three components of the angular velocity vector in this moving coordinate system. Thus, it is understood that the gyroscope 40 may in fact be a set of three gyroscopes associated with one of the three axes, in particular a three-axis gyroscope (i.e., each of which can measure one of the three components of the angular velocity vector).

[0040] Further, the motion sensor 30 includes an acquisition unit 42 for acquiring the linear velocity of the dead reckoning system 22, that is, the linear velocity of its movement. This acquisition unit 42 can acquire the linear velocity directly or indirectly and can therefore use various types.

[0041] For example, the acquisition unit 42 can be composed of one or more accelerometers (not shown). These accelerometers are preferably arranged on three axes according to a system having the same three orthogonal axes as the gyroscope 40. They are sensitive to external forces other than the gravity applied to the sensor 30 and can measure a specific acceleration. The linear velocity is obtained by time integration of this acceleration.

[0042] As a modification, when the object 12 is a vehicle with wheels, the acquisition unit 42 can be configured by at least two odometers corresponding to the wheels of the vehicle, for example, two rear wheels. An odometer means a device (a "revolution counter") that can measure the speed of a wheel by counting the number of rotations of the wheel. Generally, an odometer has a component (e.g., a magnet) fixed to the wheel, and counts the number of rotations (rotation frequency) per unit time by detecting each passage of this fixed component (referred to as the "top"). As other techniques, for example, a method of optically detecting a mark on a wheel, a magnetometer described in French Patent Application Publication No. 2939514 for detecting the rotation of a metal object such as a wheel, etc. are known. Here, the "speed" of the wheel is a scalar, that is, the norm of the speed of the wheel in the ground reference system (assuming no skidding). If the radius of the wheel is known, the speed norm can be estimated by measuring the rotation frequency.

[0043] Optionally, the motion sensor 30 may include, for example, a stride detector (not shown) for detecting when the foot of the pedestrian 12 touches the ground as described in International Publication No. 2017 / 060660.

[0044] In the illustrated example, the processing unit 32 is composed of a programmable machine such as a DSP (Digital Signal Processor) or a microcontroller. This is composed of a processor or CPU (Central Processing Unit) 44, and a memory 46 such as a RAM (Random Access Memory) and / or a ROM (Read Only Memory). The processor 44 is configured to execute instructions loaded into the memory 46. When the dead reckoning system 22 is powered on, the processor 44 can read and execute instructions from the memory 46. These instructions form a computer program that causes the processor 44 to calculate the orientation and position of the dead reckoning system 22 in a predetermined fixed coordinate system by implementing the methods described in, for example, International Publication No. 2017 / 060660 and French Patent Application Publication No. 2939514.

[0045] As a modification example (not shown), the processing unit 32 can be configured by a dedicated machine or component such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit).

[0046] In addition, the processing unit 32 includes a buffer memory 49 for temporarily storing information necessary for calculating the azimuth and position of the dead reckoning system 22 in a predetermined fixed coordinate system.

[0047] Optionally, the dead reckoning system 22 includes a network of magnetometers 48 that are linked to the box 16, that is, have substantially the same movement as the movement of the box 16 in the ground reference system and are spatially separated from each other. Each magnetometer 48 is a three-axis magnetometer that can measure the magnetic field along three axes. For this purpose, each magnetometer 48 is typically composed of three uniaxial magnetometers (not shown) oriented along axes that are substantially perpendicular to each other. These axes are preferably the same as the axes of the system having the three orthogonal axes of the gyroscope 40.

[0048] Due to its special shape, the network of magnetometers 48 can determine the spatial gradient of the measured magnetic field at each measurement time of the magnetometer 48, particularly the coefficients of this gradient along each axis of the moving coordinate system linked to the box 16. Each coefficient of the gradient can be obtained, for example, by combining with an optimization method such as the least squares method or the median filter type, or by combining with the inherent characteristics of the magnetic field described by Maxwell's equations, using the vector measurement values of the magnetic field by the magnetometer 48. However, other conventional methods adapted for calculating the coefficients of the spatial gradient of the magnetic field can also be used.

[0049] In addition, the processing unit 32 is typically configured to adjust the determination of the speed of the dead reckoning system 22 by implementing the method described in European Patent Application Publication No. 2541199.

[0050] The dead reckoning system 22 typically has an accuracy on the order of a few percent, for example 1 to 3%, with respect to the travel distance, and an accuracy on the order of several tens of degrees per hour, for example 30 to 80 degrees per hour, with respect to the azimuth drift.

[0051] The relative positioning system 24 can identify the relative position of a point fixed to the dead reckoning system 22 with respect to a reference system whose position in a predetermined fixed coordinate system is known, and derive the position of the dead reckoning system 22 in the predetermined fixed coordinate system therefrom. For this purpose, the relative positioning system 24 includes a sensor 50 suitable for measuring parameters that enable positioning of a fixed point with respect to the reference system, and a processing unit 52 for deriving the position of the dead reckoning system 22 in the predetermined fixed coordinate system from these parameters.

[0052] Preferably, the relative positioning system 24 is constituted by a multilateration system, particularly a close-range multilateration system. Further, the infrastructure 18 includes at least three beacons 54 (FIG. 2) (or "anchors") that each have a known position in a predetermined fixed coordinate system and are configured to communicate with the relative positioning system 24 at the access point 19. For this purpose, the sensor 50 is typically constituted by a wireless communication system that can communicate with the beacon 54 and estimate the distance to each beacon 54, for example, by measuring the two-way ranging (TWR) distance.

[0053] Preferably, the sensor 50 is composed of an ultra-wideband telemetry device that communicates with the beacon 54 and can measure the distance to the beacon 54 via an ultra-wideband (UWB) protocol. This enables position measurement accuracy on the order of several tens of centimeters, for example, in the range of 20 cm to 1 m. As is known to those skilled in the art, the ultra-wideband protocol is a wireless communication protocol based on the transmission of very short pulses (on the order of nanoseconds) over a wide frequency spectrum. Therefore, communication with a wide bandwidth (500 - 1,350 MHz) in the range of 0.5 - 9.5 GHz (center frequency) is possible depending on the channel used. As a variant, the sensor 50 can communicate with the beacon 54 via a Bluetooth protocol or a Wi-Fi protocol.

[0054] As a variant, the multilateration system can be composed of a GNSS system.

[0055] Furthermore, the processing unit 52 is configured to derive the relative position of the sensor 50 in a predetermined fixed coordinate system from the distance measurement values and the known position of the beacon 54, typically by multilateration or optimization, and to derive the position of the dead reckoning system 22 in the predetermined fixed coordinate system from this position and the position of the sensor 50 relative to the dead reckoning system 22.

[0056] According to another embodiment (not shown), the relative positioning system 24 is composed of a triangulation system. The sensor 50 can measure at least one angle between the observation directions of two beacons each having a known position in a predetermined fixed coordinate system. Furthermore, the processing unit 52 is configured to derive the position of the sensor 50 in the predetermined fixed coordinate system from the angle measurement values and the known position of the beacon 54, typically by triangulation, and to derive the position of the dead reckoning system 22 in the predetermined fixed coordinate system from this position and the position of the sensor 50 relative to the dead reckoning system 22.

[0057] According to yet another embodiment (not shown), the relative positioning system 24 is constituted by a map matching system. The sensor 50 can measure environmental parameters such as terrain and magnetic field, and the processing unit 52 can collate this measurement value with a map of parameters stored in the memory, thereby deriving the position of the sensor 50 in a predetermined fixed coordinate system.

[0058] According to a fourth embodiment (not shown), the relative positioning system 24 is constituted by a visual positioning system. The sensor 50 is constituted by an imager associated with an image processing system. The imager is configured to acquire an image of the environment, and the processing system is configured to detect feature points such as target markers whose positions are known in a predetermined fixed coordinate system in each image. Further, the processing unit 52 is configured to derive the relative position of the sensor with respect to the feature point, and derive the position of the dead reckoning system 22 in a predetermined fixed coordinate system from this relative position, the known position of the feature point, and the position of the sensor 50 with respect to the dead reckoning system 22.

[0059] As a modification (not shown), the imager may be fixed (provided in the infrastructure 18), and the feature points (generally, target markers) may be attached to the box 16. As a further modification, a plurality of targets may be attached to the box 16 to provide a motion capture configuration detected by a fixed imager.

[0060] In the illustrated example, the processing unit 52 is constituted by a programmable machine such as a DSP (Digital Signal Processor) or a microcontroller. This is composed of a processor or CPU (Central Processing Unit) 56 and a memory 58 such as RAM (Random Access Memory) and / or ROM (Read Only Memory). The processor 56 is configured to execute instructions loaded into the memory 58. When the relative positioning system 24 is powered on, the processor 56 can read and execute instructions from the memory 58. These instructions form a computer program that causes the processor 56 to calculate the orientation and position of the dead reckoning system 22 in a predetermined fixed coordinate system from the parameters measured by the sensor 50.

[0061] As a modification (not shown), the processing unit 52 can be constituted by a dedicated machine or component such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit).

[0062] In the illustrated example, the processing unit 52 of the relative positioning system 24 is separate from the processing unit 32 of the dead reckoning system 22. As a modification (not shown), these processing units 32, 52 may be integrated.

[0063] The communication system 26 is configured to perform short-range wireless communication such as Bluetooth or Wi-Fi (especially, in one embodiment, with the mobile terminal 29) and / or to connect to a mobile network (such as UMTS / LTE / 5G etc.) for long-distance communication. As a modification (not shown), the communication system 26 may be a wired connection technology (such as USB etc.) for transferring data from the storage module 28 to another storage module, for example, the mobile terminal 29.

[0064] For example, the communication system 26 is configured to transmit the position calculated by the dead reckoning system 22 to the mobile terminal 29, and the position is displayed on the interface of the navigation software by the mobile terminal 29.

[0065] In the above example, the processing units 32 and 52 of the dead reckoning system 22 and the relative positioning system 24 are integrated in the box 16. As a modification (not shown), at least a part of these processing units 32 and 52 may be, for example, in the mobile terminal 29, in the infrastructure 18, and / or in a remote server (not shown). In other words, at least a part of the step of calculating the position of the dead reckoning system 22 by the dead reckoning system 22 or the relative positioning system 24 may be executed by the mobile terminal 29, the infrastructure 18, and / or the remote server. Further, the communication system 26 may be configured to transmit the data from the motion sensor 30 and / or the data from the sensor 50 to the mobile terminal 29, the infrastructure 18, and / or the remote server. Advantageously, the communication system 26 is further configured to receive the position of the dead reckoning system 22 calculated by the dead reckoning system 22 or the relative positioning system 24 from the mobile terminal 29, the infrastructure 18, and / or the remote server.

[0066] Returning to FIGS. 1 and 2, the infrastructure 18 includes a plurality of beacons 54 arranged at the access points 19 to the predefined space 14 as described above. These beacons 54 are typically integrated into a portal arranged at the access point 19, such as the portal 60 shown in FIG. 2. Each portal 60 includes at least three beacons 54 so that the pedestrian 12 passing through the access point 19 can be positioned by multilateration.

[0067] The infrastructure 18 is, for example, a permanent fixed infrastructure. This is particularly applicable when the predefined space 14 is an industrial site and the positioning system 10 is intended to track the movement of technicians at the site. Alternatively, the infrastructure 18 may be a temporary fixed infrastructure. This is the case, for example, when the predefined space 14 is an intervention site, particularly a fire site, and the infrastructure 18 is carried to and installed at one of the access points of the intervention site before the arrival of the firefighters. Alternatively, the infrastructure 18 may be a mobile infrastructure. This is typically mounted on a vehicle (not shown) used for transporting the pedestrian 12, and the movement of the pedestrian 12 to the predefined space 14 is tracked. This vehicle itself is equipped with a position and orientation system that enables the calculation of the position of the infrastructure 18 in a given coordinate system. This is applicable, for example, when the predefined space 14 is an intervention site, particularly when a hostage situation has occurred.

[0068] Next, a method 100 implemented by the positioning system 10, more specifically the processing units 32, 52, will be described with reference to FIGS. 4 to 6.

[0069] As shown in FIG. 4, the method 100 starts with a first step 102 of activating the dead reckoning system 22. This first step 102 is generally carried out while the pedestrian 12 wearing the box 16 is still outside the predefined space 14. Typically, step 102 is initiated when the pedestrian 12 presses a button (not shown) on the box 16. The relative positioning system 24 generally starts at the same time as step 102.

[0070] JPEG2025524594000002.jpg57170

[0071] In addition, the dead reckoning system 22 provides the azimuth ψ(t) of the pedestrian 12 in any of the above-mentioned fixed coordinate systems. This arbitrary fixed coordinate system is a horizontal two-dimensional coordinate system and is typically composed of a rotation of an angle θ about the vertical axis of a predetermined fixed coordinate system and a translation of a horizontal vector Δ. Therefore, the arbitrary fixed coordinate system shares the vertical axis as a common axis with the predetermined fixed coordinate system. This is arbitrarily selected by the dead reckoning system 22 according to the posture at startup.

[0072] JPEG2025524594000003.jpg57170

[0073] After step 102, step 104 is performed to determine whether the relative positioning system 24 can evaluate the position of the dead reckoning system 22 in a predetermined fixed coordinate system. If the determination is affirmative, that is, typically when the box 16 is within the range of the beacon 54 of the infrastructure 18, after step 104, step 106 to readjust the position of the dead reckoning system 22 and step 107 to readjust the azimuth of the dead reckoning system 22 follow. If the determination is negative, that is, typically when the box 16 is outside the range of the beacon 54 of the infrastructure 18, step 104 is repeated after a lapse of time.

[0074] JPEG2025524594000004.jpg37170

[0075] Referring to FIG. 5, the azimuth readjustment 107 starts from the fact that while the box 16 is within the range of the beacon 54 of the infrastructure 18, the pedestrian 12 moves 110 along the movement trajectory during the movement section.

[0076] During the movement 110, in the azimuth readjustment 107, for a plurality of evaluation times i k included in the movement section, the relative positioning system 24 evaluates 112 the position u(i k ) of the pedestrian 12 in the predetermined fixed coordinate system at each evaluation time i k . After the evaluation 112, the dead reckoning system 22 becomes the evaluation position u(i k) is received 114.

[0077] In parallel, in the azimuth readjustment 107, for a plurality of estimated times τ k included in the movement section, the position v(τ k ) of the pedestrian 12 in an arbitrary fixed coordinate system of each estimated time τ k ) is estimated 116.

[0078] After the reception step 114 and the estimation step 116, a step 118 of matching the estimated position v(τ k ) with the evaluated position u(i k ) by the dead reckoning system 22 is performed. The readjustment step 107 is based on the use of the evaluated position synchronized with the estimated position. However, these two types of positions come from different origins, and generally the evaluation time i k is different from the estimated time τ k . Therefore, it is necessary to match the evaluated position u(i k ) with the estimated position v(τ k ). This matching aims to associate and synchronize the evaluated position u(i k ) with the estimated position v(τ k ) so that a pair of the evaluated position u(t k ) and the estimated position v(t k ) can be formed for a plurality of decision times t k ). Therefore, this matching is for the set {ui k} of the evaluated positions u(i k ) at the evaluation time i k and the set {v(τ k )} of the estimated positions v(τ k ) at the estimated time τ k to select, from k · the set {u(t k )} of the evaluated positions u(t k ) at the decision time t · and, k · the estimated positions v(t k ) at the decision time t k )} k and, · the estimated positions v(t k ) at the decision time tk ) set of {v(t k )} k is derived from.

[0079] Therefore, the matching is, for example, · Evaluation time i k at the evaluation position u(i k ) set of {u(i k )} k and, · Estimation time τ k at the estimated position v(τ k ) set of {v(τ k )} k includes interpolating at least one set or subset of.

[0080] This interpolation preferably uses a time spline. The spline used for interpolation is preferably at least twice differentiable. In particular, the spline representation makes it possible to enforce the continuity of the trajectory. Also, with the spline representation, it is possible to optimize various related parameters, especially the time synchronization parameters, using a method based on the gradient of the criterion to be minimized. As a variant, for example, another approach such as a discrete representation of the trajectory along which the pedestrian 12 moves can be used to perform the interpolation.

[0081] Preferably, only the set of estimated positions {v(τ k )} k is interpolated, and the decision time t k is selected to be equal to the evaluation time i k (t k = i k ). Therefore, the set of evaluation positions u(t k ) at the decision time t k ) set of {u(t k )} k is merged with the set of evaluation positions u(i k ) at the evaluation time i k ) set of {u(i k )} k In this case, the evaluation position u(i k ) at the evaluation time i kThe reception 114 of k obtains the estimated position u(t k ) of the pedestrian 12 at the determination time t

[0082] As a modification, only the set of estimated positions {u(t k )} k is interpolated, and the determination time t k may be selected to be equal to the estimation time τ k (t k =τ k ). In this case, the matching 118 obtains the estimated position u(t k ) of the pedestrian 12 at the determination time t k .

[0083] When the evaluation time i k corresponds to the evaluation time τ k , the matching 118 is limited to associating the estimated position u(i k ) and the estimated position v(τ k ) at the same times i k , τ k . Note that this becomes the determination time t k .

[0084] After the matching 118, the pair of the matched positions u(t k ), v(t k ) is added 120 to the buffer memory 49, and the cleaning 122 of the buffer memory 49 is performed. In the cleaning 122, the pair of the positions u(t k ), v(t k ) associated with the determination time t k not within the moving interval is deleted from the buffer memory 49. The moving interval is understood here as a sliding time window of a predetermined time ending at the date of the latest evaluation time i k or the estimation time τ k .

[0085] After steps 120 and 122, step 124 of deriving the orientation readjustment parameter θ is performed. Here, the orientation readjustment parameter θ is constituted by, in particular, an angle, which is a parameter for correcting the orientation by rotation around the axis of a predetermined coordinate system (the vertical axis in this example). Further, the orientation readjustment 107 may include, in parallel with step 124, other steps (not shown) of deriving other orientation readjustment parameters, for example, parameters for correcting the deviation of the angular change of the orbit.

[0086] Referring to FIG. 6, step 124 includes a first sub-step 130 of calculating a first candidate value θ1 of the orientation readjustment parameter θ using all the points included in the buffer memory 49, that is, all pairs of positions u(t k ) and v(t k ). This first candidate value θ1 is calculated by minimizing a cost function that compares the N evaluation positions u(t k ) corresponding to the pairs of positions u(t k ) and v(t k ) included in the buffer memory 49 at N decision times t k with the estimated positions v(t k ) at the above N decision times t k ) corrected by the orientation readjustment parameter θ and the position readjustment parameter Δ. This cost function represents, in particular, the average geometric deviation between the evaluation positions {u k} k after applying the following geometric transformation to the evaluation positions {u k} k and the estimated positions {v k} k . · A rotation by an angle equal to the orientation readjustment parameter θ around the axis of a predetermined coordinate system (the vertical axis in this example), and · A translation of a vector equal to the position readjustment parameter Δ in a direction orthogonal to the above axis.

[0087] JPEG2025524594000005.jpg8170 where {u k} k is a set of evaluation positions at the decision times. {v k} k is a set of estimated positions at the decision time. {t k} k is a set of decision times. N is the number of pairs of positions u(t k ), v(t k ) contained in the buffer memory 49. θ is an azimuth readjustment parameter. R(θ) is a rotation matrix representing the correction of the attitude by the application of the azimuth readjustment parameter. Δ is a position readjustment parameter.

[0088] Therefore, the first candidate value θ1 is as follows. JPEG2025524594000006.jpg27170 where u1(t k ) is the first position coordinate along the first axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000007.jpg11170u2(t k ) is the second position coordinate along the second axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000008.jpg11170v1(t k ) is the first position coordinate along the first axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000009.jpg11170v2(t k ) is the second position coordinate along the second axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000010.jpg11170N is the number of pairs of positions u(t k ), v(t k ) contained in the buffer memory 49.

[0089] JPEG2025524594000011.jpg57170

[0090] After this sub-step 130, a sub-step 132 for evaluating the first accuracy value σ associated with the first candidate value θ1 follows. The first accuracy value σ θ1 is, for example, a function of the uncertainties σ θ1 of the estimated position {v k} k and the evaluation position {u k}. k Typically, this consists of the standard deviation of the first candidate value θ1 and is given by the following formula. v σ u In the formula, {u JPEG2025524594000012.jpg24170{u k} k is the set of evaluation positions at the decision time. {v k} k is the set of estimated positions at the decision time. {t k} k is the set of decision times. σ u 2 is the variance of the random error affecting each coordinate of the evaluation position by the relative positioning system 24. σ v 2 is the covariance of the random error affecting each coordinate of the estimated position by the dead reckoning system 22. u1(t k ) is the first position coordinate along the first axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . u2(t k ) is the second position coordinate along the second axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . v1(t k ) is the first position coordinate along the first axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . v2(t k ) is the second position coordinate along the second axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000013.jpg42170N is the number of pairs of positions u(t k ), v(t k ) contained in the buffer memory 49.

[0091] As a modification, the first accuracy value σ θ1 may be a function of the average deviation observed between the set of evaluation positions {u k} k and the set of associated estimated positions {v k} k corrected by the azimuth readjustment parameter θ having the first candidate value θ1. This is given, for example, by the following equation. JPEG2025524594000014.jpg21170Where N is the number of pairs of positions u(t k ), v(t k ) contained in the buffer memory 49. {u k} k is the set of evaluation positions at the decision time. {v k} k is the set of estimated positions at the decision time. JPEG2025524594000015.jpg21170θ1 is the azimuth readjustment parameter assigned to the first candidate value θ1. R(θ1) is the rotation matrix representing the correction of the attitude by the application of the azimuth readjustment parameter.

[0092] As a modification, the first accuracy value σ θ1 is a function of the uncertainties σ k}, σ k of the estimated positions {v k} k and the evaluation positions {u v , σ u}, as well as the set of evaluation positions {u k} k and a set of associated estimated positions {v corrected by an orientation adjustment parameter θ having a first candidate value θ1 k} k It may be a combination of functions of the average deviation observed between.

[0093] Derivation step 124, in parallel with substeps 130 and 132, without using the oldest point included in buffer memory 49, i.e., the pair of positions u(t1), v(t1) associated with the oldest decision time t1, uses all pairs of positions u(t k ), v(t k ) included in buffer memory 49 to further include substep 134 of calculating a second candidate value θ2 of the orientation adjustment parameter θ. This second candidate value θ2 corresponds to the N - 1 decision times t k ) corresponding to the pair of most recent positions u(t k ), v(t k ) of the evaluation positions u(t k ) and the N - 1 decision times t k ) corrected by the orientation adjustment parameter θ and the position adjustment parameter Δ. It is calculated by minimizing a cost function that compares the estimated positions v(t k ). This cost function, in particular, represents the average geometric deviation between the evaluation positions {u k} k after applying the following geometric transformation to, and the estimated positions {v k} k : k} k : · A rotation by an angle equal to the orientation adjustment parameter θ about an axis (here, the vertical axis) of a predetermined coordinate system, and · A translation of a vector equal to the position adjustment parameter Δ in a direction orthogonal to the above axis.

[0094] JPEG2025524594000016.jpg9170 where {u k} k is the set of evaluation positions at the decision time. {v k}k is a set of estimated positions at the decision time. {t k} k is a set of decision times. N is the number of pairs of positions u(t k ), v(t k ) included in the buffer memory 49. θ is an azimuth readjustment parameter. R(θ) is a rotation matrix representing the modification of the attitude by the application of the azimuth readjustment parameter. Δ is a position readjustment parameter.

[0095] Therefore, the second candidate value θ2 is as follows. JPEG2025524594000017.jpg26170 where u1(t k ) is the first position coordinate along the first axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000018.jpg11170u2(t k ) is the second position coordinate along the second axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000019.jpg11170v1(t k ) is the first position coordinate along the first axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000020.jpg11170v2(t k ) is the second position coordinate along the second axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000021.jpg11170N is the number of pairs of positions u(t k ), v(t k ) included in the buffer memory 49.

[0096] JPEG2025524594000022.jpg59170

[0097] After this sub-step 134, a sub-step 136 for evaluating a second accuracy value σ related to a second candidate value θ2 follows. The second accuracy value σ θ2 is, for example, a function of the uncertainties σ θ2 of the estimated position {v k} k and the evaluation position {u k}. k Typically, this is the standard deviation of the second candidate value θ2 and is given by the following equation. v σ u In the formula, {u JPEG2025524594000023.jpg26170{u k} k is the set of evaluation positions at the decision time. {v k} k is the set of estimated positions at the decision time. {t k} k is the set of decision times. σ u 2 is the covariance of the random errors affecting each coordinate of the evaluation position by the relative positioning system 24. σ v 2 is the covariance of the random errors affecting each coordinate of the estimated position by the dead reckoning system 22. u1(t k ) is the first position coordinate along the first axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . u2(t k ) is the second position coordinate along the second axis of a predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t k . v1(t k ) is the first position coordinate along the first axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . v2(t k ) is the second position coordinate along the second axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t k . JPEG2025524594000024.jpg42170N is the number of pairs of positions u(t k ), v(t k ) included in the buffer memory 49.

[0098] As a modification example, the second accuracy value σ θ2 may be a function of the average deviation observed between the set of evaluation positions {u k} k and the set of related estimated positions {v k} k corrected by the azimuth readjustment parameter θ having the second candidate value θ2. This is given, for example, by the following formula. JPEG2025524594000025.jpg21170In the formula, N is the number of pairs of positions u(t k ), v(t k ) included in the buffer memory 49. {u k} k is the set of evaluation positions at the decision time. {v k} k is the set of estimated positions at the decision time. JPEG2025524594000026.jpg21170θ2 is the azimuth readjustment parameter assigned to the second candidate value θ2. R(θ2) is the rotation matrix representing the correction of the attitude by the application of the azimuth readjustment parameter.

[0099] As a modification example, the second accuracy value σ θ2 is a function of the uncertainties σ k of the estimated positions {v k} and the evaluation positions {u k}, σ k , as well as the set of evaluation positions {u v}, σ u . k}k and a set of associated estimated positions {v corrected by an orientation adjustment parameter θ having a first candidate value θ2 k} k may be a combination of functions of the average deviation observed therebetween.

[0100] After substeps 132 and 136, a substep 140 is performed to compare a first accuracy value σ θ1 with a second accuracy value σ θ2 If the first accuracy value σ

[0101] reflects better accuracy than the second value σ θ1 i.e., σ θ2 < σ θ1 < σ θ2 then following step 140, a step 142 is performed to select the first candidate value θ1 as the selected candidate value.

[0102] JPEG2025524594000027.jpg26170

[0103] Thereby, the accuracy of the adjustment parameter θ can be maximized.

[0104] Optionally, after substep 142, a series of substeps 150-154 for verifying a new adjustment parameter θ from the residual calculation follow.

[0105] After substep 142, a substep 150 is performed to calculate an expected adjustment residual r exp This adjustment residual is intended to reflect the expected average deviation between a set of evaluation positions {u k} k and a set of associated estimated positions {v k} k corrected by the orientation adjustment parameter θ. This is given by the following equation. JPEG2025524594000028.jpg16170where N is the position u(t k ) contained in the buffer memory 49, v(tk is the number of pairs of σ u 2 is the covariance of the random errors affecting each coordinate of the evaluation position by the relative positioning system 24. σ v 2 is the covariance of the random errors affecting each coordinate of the estimated position by the dead reckoning system 22. σ θ 2 is the covariance of the readjustment parameter θ and is equal to the square of the above-mentioned standard deviation σ of the selected candidate value θ1. θ1 of u1(t k ) is the first position coordinate along the first axis of the predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t. k u2(t k ) is the second position coordinate along the second axis of the predetermined fixed coordinate system of the evaluation position of the dead reckoning system 22 at the decision time t. k v1(t k ) is the first position coordinate along the first axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t. k v2(t k ) is the second position coordinate along the second axis of an arbitrary fixed coordinate system of the estimated position of the dead reckoning system 22 at the decision time t. k JPEG2025524594000029.jpg42170

[0106] After sub-step 150, sub-step 152 for calculating the observed readjustment residual r obs is performed. This readjustment residual is intended to reflect the average deviation observed between the set of evaluation positions {u k} k and the set of related estimated positions {v k} k corrected by the azimuth readjustment parameter θ assigned to the selected candidate value θ1. This is given by the following equation. ​​​​JPEG2025524594000030.jpg16170 where N is the position u(t k ), v(t k ) pairs. {u k} k is the set of evaluation positions at the decision time. {v k} k is the set of estimated positions at the decision time. JPEG2025524594000031.jpg21170θ is the reorientation parameter. R(θ) is the rotation matrix that represents the attitude modification due to application of the reorientation parameters.

[0107] After substep 152, the observation realignment residual r obs and the predicted rebalanced residual r increased by a given threshold δ exp A substep 154 is performed in which the

[0108] Observation rescaled residual r obs is the predicted rescaled residual r exp and the threshold δ, i.e., if the inequality r obs >r exp If +δ holds, sub-step 154 is followed by sub-step 156 of rejecting the selected candidate value, after which the readjustment parameter θ retains its previous value.

[0109] Observation rescaled residual r obs is the predicted rescaled residual r exp and the threshold δ, that is, if the inequality r obs ≦r exp +δ holds, after substep 154, the accuracy value σ associated with the selected candidate value θn the precision value σ associated with the current value of the retuning parameter θ θo Given a candidate value selection algorithm, a precision value σ associated with the selected candidate value is then compared with the θn is the first precision value σ θ1Note that this is equivalent to

[0110] More specifically, in sub-step 158, the accuracy value σ associated with the selected candidate value is calculated. θn and an accuracy value σ associated with the current value of the readjustment parameter θ increased by a time drift value proportional to the elapsed time between the determination of the selected candidate value and the determination of the current value of the orientation readjustment parameter θ. θo This time drift value is typically calculated as (t n -t o ) × b. In the formula, t n is the decision time {t k} k is the time represented. t o is the decision time {t k} k is the time represented. b is a predefined constant representing a typical bias value of the gyrometer 40 .

[0111] Each time t o , t n are, for example, the decision times {t k} k As a variant, at each time t o , t n is, for example, the most recent decision time t considered for determining the selected candidate value and for determining the current value of the orientation readjustment parameter θ, respectively. k may be.

[0112] If substep 158 is performed for the first time since the dead reckoning system 22 has been started, the accuracy value σ associated with the current value of the recalibration parameter θ is θo is preferably infinite.

[0113] After sub-step 158, a sub-step 160 is performed to determine the future value of the readjustment parameter θ according to the result of the comparison 158. This future value is typically a function of the candidate value and the current value.

[0114] For example, if the accuracy value σ θn associated with the selected candidate value is greater than or equal to the accuracy value σ θo associated with the current value increased by only the time drift value, that is, if the inequality σ θn ≧σ θo +(t n -t o )×b holds, the future value is equal to the current value. On the other hand, if the accuracy value σ θn associated with the selected candidate value is strictly smaller than the accuracy value σ θo associated with the current value increased by only the time drift value, that is, if the inequality σ θn <σ θo +(t n -t o )×b holds, the future value is equal to the selected candidate value.

[0115] As a variant, the future value may be equal to a combination of the selected candidate value and the current value as a function of the ratio of the accuracy value σ θn associated with the selected candidate value, typically applied with a Kalman filter or Bayesian fusion, to the accuracy value σ θo associated with the current value increased by the time drift. For example, the future value may be equal to the following. JPEG2025524594000032.jpg12170where θ n is the selected candidate value. σ θn is the accuracy value associated with the above-selected candidate value. θ o is the current value of the azimuth readjustment parameter θ. σ θo is the accuracy value associated with the above current value.

[0116] With this sub-step 160, step 124 of deriving the azimuth readjustment parameter θ ends.

[0117] Returning to FIG. 5, after step 124, step 126 of adjusting the evaluation azimuth is performed. In this step 126, the dead reckoning system 22 adjusts the estimated azimuth by applying the azimuth readjustment parameter θ. JPEG2025524594000033.jpg6170

[0118] With this step 126, the azimuth readjustment step 107 ends.

[0119] Returning to FIG. 4, after the position readjustment step 106 and the azimuth readjustment step 107, step 109 of calculating the position of the pedestrian 12 by the dead reckoning system 22 is performed. In this step 109, the dead reckoning system 22 applies the azimuth readjustment parameter and the position readjustment parameter to determine the position of the pedestrian 12 in a predetermined coordinate system. This position is obtained by the following formula. JPEG2025524594000034.jpg16170v(t) is the position of the pedestrian 12 in an arbitrary coordinate system estimated by the dead reckoning system 22. θ is the azimuth readjustment parameter. R(θ) T is a rotation matrix representing the correction of the posture by applying the inverse matrix of the azimuth readjustment parameter. Δ is the position readjustment parameter.

[0120] JPEG2025524594000035.jpg11170

[0121] In parallel, method 100 returns to step 104 to enable continuous updating of the readjustment parameters of the azimuth θ and the position Δ each time the pedestrian 12 passes near the beacon 54.

[0122] According to the above-described exemplary embodiments, the movement of people in a building can be accurately tracked in a simple and economical way. This objective is achieved by a particularly lightweight infrastructure 18 that only needs to be deployed and a particularly simple positioning box 16 that only needs to be worn by the person to be tracked. The motion sensors 30 provided in these boxes 16 can periodically readjust the dead reckoning system every time a person passes near the infrastructure 18, so they do not need to be very accurate.

[0123] In particular, the application of UWB technology to the relative positioning system 24 is particularly advantageous because the error of UWB positioning is small, enabling very accurate readjustment for a short movement range, thereby significantly reducing the requirements for the infrastructure. Also, the pedestrian 12 is completely transparent to the pedestrian 12 because there is no need for the pedestrian 12 to perform special operations to ensure the readjustment of their position information box 16.

Claims

1. An attitude readjustment method (107) for readjusting the attitude provided by a dead reckoning system (22), Step (116) of the dead reckoning system (22) to estimate the estimated position of the dead reckoning system (22) in an arbitrary fixed coordinate system for each of a plurality of determination times included in the movement section along the movement trajectory of the dead reckoning system (22), For each determination time, the steps include: (114) obtaining the evaluation position of the dead reckoning system (22) in a predetermined fixed coordinate system at the determination time, evaluated by the relative positioning system (24); A posture readjustment method (107) comprising the step (124) of deriving at least one posture readjustment parameter by minimizing a cost function that compares the evaluation position with the estimated position corrected by each readjustment parameter.

2. The posture readjustment method (107) according to claim 1, wherein the dead reckoning system (22) is worn by a pedestrian (12), and preferably the dead reckoning system (22) is worn on the foot or ankle of the pedestrian (12).

3. The attitude readjustment method (107) according to claim 1, wherein the dead reckoning system (22) comprises a motion sensor (30) for measuring the movement of the dead reckoning system (22) and a processing unit (32) for deriving the attitude and position of the dead reckoning system (22) from the measured movement.

4. The relative positioning system (24) is selected from a polygonal positioning system, a multi-sided positioning system, a map matching system, and a visual positioning system, and preferably the relative positioning system (24) includes an ultra-wideband telemetry device, as described in claim 1 of the attitude readjustment method (107).

5. The posture readjustment method (107) according to claim 1, wherein the arbitrary fixed coordinate system and the predetermined fixed coordinate system have a common axis, and the posture readjustment parameter is composed of a parameter for correcting posture by rotation around the common axis, preferably by an angle.

6. The posture readjustment method (107) according to claim 1, wherein the cost function represents the mean geometric deviation between the evaluation position and the estimated position after applying a geometric transformation including rotation and preferably translation to the evaluation position or the estimated position.

7. The posture readjustment method (107) according to claim 1, wherein the step of deriving the readjustment parameter includes calculating candidate values ​​for the readjustment parameter (130), evaluating the precision value of the candidate values ​​(132), comparing the precision value with the previous precision value related to the previous readjustment parameter (158), and determining the readjustment parameter according to the result of the comparison (158) (160), the readjustment parameter depends on the candidate value and the previous readjustment parameter.

8. The posture readjustment method (107) according to claim 7, wherein the accuracy value is a function of the uncertainty of the estimated position and the evaluation position, and / or a function of the mean geometric deviation between the evaluation position and the estimated position after applying the candidate values ​​of the readjustment parameter.

9. The step (124) of deriving the aforementioned readjustment parameters is, a) A substep (130) which calculates a first candidate value for the readjustment parameter by minimizing a cost function that compares the evaluation position for N decision times with the estimated position for N decision times corrected by the readjustment parameter, b) A substep (132) for calculating a first precision value related to the first candidate value, c) A substep (134) to calculate a second candidate value for the readjustment parameter by minimizing a cost function that compares the evaluation position for N-1 decision times obtained by subtracting the oldest decision time from the N decision times with the estimated position for the N-1 decision times corrected by the readjustment parameter, d) A substep (136) for calculating a second precision value related to the second candidate value, e) A substep (140) of comparing the first precision value with the second precision value, f) A posture readjustment method (107) according to claim 1, comprising a substep (142) of selecting a first candidate value if the first accuracy value reflects the best accuracy.

10. If the accuracy value that reflects the best accuracy is comprised of the second accuracy value, the step (124) of deriving the readjustment parameter is: A substep (144) to delete the evaluation position and the estimated position for the oldest determination time, A posture readjustment method (107) according to claim 9, which includes a substep of repeating substeps a) to e) by decreasing N by 1.

11. A method (100) for determining the position of an object (12) equipped with a dead reckoning system (22) in a predefined space (14), Step (102) of starting the dead reckoning system (22), Step (107) of readjusting the attitude of the dead dead navigation system (22) using a relative positioning system (24) including infrastructure (18) installed at an access point (19) in the predefined space (14) to acquire attitude readjustment parameters, wherein the attitude readjustment method described in any one of claims 1 to 10 is performed, Step (106) of readjusting the position of the dead dead navigation system (22) using a relative positioning system (24) including infrastructure (18) installed at an access point (19) in the predefined space (14) to acquire position readjustment parameters, A position determination method (100) comprising the step (109) of calculating the calculated position of the dead reckoning system (22) in a predetermined coordinate system using the attitude readjustment parameter and the position readjustment parameter.

12. The positioning method (100) according to claim 11, wherein the infrastructure (18) is mounted on the vehicle itself, which is equipped with a position and attitude system that enables the calculation of the position of the infrastructure (18) in the predetermined coordinate system.

13. Dead reckoning system (22), The dead reckoning navigation system (22) comprises a motion sensor (30) for measuring the movement of the dead reckoning navigation system (22), and a processing unit (32) for deriving the attitude and position of the dead reckoning navigation system (22) in a fixed coordinate system from the measured movement, wherein the processing unit is configured to perform the attitude readjustment method (107) according to any one of claims 1 to 10 in order to readjust the attitude.

14. A computer program product that includes code instructions for performing the posture readjustment method (107) according to any one of claims 1 to 10 when the program is executed by a processor.

15. A storage means readable by a computer device on which a computer program product containing code instructions for performing the posture readjustment method (107) according to any one of claims 1 to 10 is recorded.

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