EP3155372A1

EP3155372A1 - Method and system for correcting measurement data and/or navigation data of a sensor base system

Info

Publication number: EP3155372A1
Application number: EP15728488.6A
Authority: EP
Inventors: Nico Steinhardt
Original assignee: Continental Teves AG and Co OHG
Current assignee: Continental Teves AG and Co OHG
Priority date: 2014-06-11
Filing date: 2015-06-08
Publication date: 2017-04-19
Also published as: DE102014211176A1; WO2015189144A1

Abstract

The invention relates to a method for correcting measurement data and/or navigation data of a sensor base system, wherein the sensor base system and at least one further sensor system detect measurement data, wherein the measurement data describes navigation data directly and/or indirectly, wherein indirectly described navigation data is calculated on the basis of the measurement data and/or on the basis of known physical and/or mathematical relationships, wherein the navigation data comprises at least one speed, wherein fault values of the measurement data and/or of the navigation data of the sensor base system are determined by means of the measurement data and/or the navigation data of the at least one further sensor system, wherein the fault values are corrected by applying corrections, wherein the sensor base system detects in vehicle-fixed or in topocentric coordinates, wherein the at least one further sensor system also detects in vehicle-fixed or in topocentric coordinates and wherein the fault values of at least the speed are determined in vehicle-fixed coordinates. The invention additionally relates to a corresponding system and to a use for the system.

Description

Method and system for correcting measurement data and / or navigation data of a sensor-based system

The invention relates to a method for the correction of measurement data and / or navigation data of a sensor base system according to the preamble of claim 1, a system for the correction of measurement data and / or navigation data of a sensor base system according to the preamble of claim 11 and a use of the system.

All measurement data is subject to errors and in many cases there is no consistent availability of the measurement data. In addition, the measured data often depends on ambient conditions. Furthermore, different sensors or sensor systems generally have different time acquisition rates, are not synchronized with other sensors or sensor systems and have a latency period between the measurement and the output of the measurement data. Sensor errors or measurement errors can be in quasi-stationary, constant over several measurements shares, such as. an offset, and stochastic, measurement-to-measurement, random components, e.g. Noise, subdivide. While the random parts are in principle not deterministically correctable, quasi-stationary errors can generally be given

Correct observability. Uncorrectable significant errors can usually be avoided, given the detectability.

In the prior art sensor fusion methods are already known in this context, which are usually also suitable for correcting or filtering measurement data from different sensors or sensor systems. In the automotive sector in particular, special requirements have to be taken into account, since a large number of different sensors have a common environment or situation. detects a motor vehicle condition by means of different measuring principles and this environment situation or this motor vehicle state by means of a Variety of different measurement data describes. For a sensor fusion applicable in the automotive sector, the greatest possible robustness against incidental disturbances and identification and compensation of systematic errors is required. Likewise, temporal influences on the measured data must be corrected and temporary failures or the unavailability of sensors bridged.

DE 10 2012 216 215 A1 describes a sensor system which comprises a plurality of sensor elements and a signal processing device. The signal processing device is designed so that it evaluates the sensor signals of the sensor elements at least partially together. Furthermore, the signal processing device is designed such that the

Measurement data of physical quantities each time information is assigned, which includes information about the time of each measurement directly or indirectly, wherein the signal processing device takes into account this time information at least in the generation of a fusion data set in a fusion filter. For the generation of the fusion data set, measurement data are used which either have matching time information or-if no measurement data with matching time information is available-a corresponding measured value with the required time information is created by means of interpolation. Furthermore, the fusion filter assumes that error values of the measured data change only negligibly over a defined period of time.

From DE 10 2010 063 984 AI a sensor system comprising several sensor elements is known. The sensor elements are formed so that they cover at least partially different primary measured variables and at least partially under use ^¬ schiedliche measuring principles. From the primary measured variable of the sensor elements, at least in part further measured quantities are derived. Furthermore, the sensor system comprises a signal processing device, an interface device and a plurality of functional devices. The sensor elements as well as all functional devices are connected to the signal Processing device connected. The primary measured variables thus provide redundant information that can be compared with one another in the signal processing device or can support one another. From the comparison of the observables calculated on different ways, conclusions can be drawn on the reliability and accuracy of the observables. The signal processing device qualifies the accuracy of the observables and makes the observables, together with an accuracy specification, available to various functional devices via an interface device.

DE 10 2012 216 211 A1 describes a method for selecting a satellite, wherein the satellite is a satellite of a global navigation system. Before such a satellite is used to determine the position or of a vehicle, the received GNSS signals are plausibilized in different ways. Each un ^¬ terschiedliche redundancies or known relationships from ^¬ be used for this verification. For example, DE 10 2012 216 211 A1 discloses, for example, determining from the signal of a satellite both the distance of the vehicle to the satellite and the relative speed of the vehicle to the satellite. The distance can be determined by means of the transit time of the signal, while the Relativge ^¬ speed can be determined by means of a phase measurement of the signal. Since the distance and the relative speed depend on each other, they can be verified against each other. Furthermore, a verification of the values determined from the signal can take place against known boundary conditions, since a vehicle usually travels within a certain speed frame. It is also described that when several signals from different satellites are received, the distances to several satellites are determined and these distances are simultaneously verified by means of trigonometric correlations and the known distance of the satellites from one another. Finally, a verification of the distance determined from the signal or the speed determined from the signal by means of other sensors, which likewise permit a position determination or speed determination, is also possible. possible. Provided that the signals of a satellite can not be verified, this satellite is not used for Positionsbe ^¬ humor or to the velocity determination. However, the generic methods and sensor systems known in the prior art are disadvantageous in that corrections of the specific physical quantities of the so-called sensor base system can only be applied without falsification of the filter states of the fusion filter or the stochastic model used, if all so that related states are known. For example, a correction of the velocity measured in so-called body coordinates (eg via odometry) in navigation coordinates is only possible if the alignment angles between the different coordinate systems are known at the same time. If the speed alone is measured in vehicle-fixed coordinates, it would be possible, even with an orientation angle with arbitrarily large error by transformations, to apply the correction of the speed in the strapdown algorithm, however, improved by the measurement system uncertainties (variances) of the speed in this case more the actual error since they do not take into account the orientation angle error. This means that in addition of other measurements that can correct the misalignment (eg GPS), which pointing in the wrong direction speed Cor ^¬ corrections are erroneously attached to other states assumed to be safe and secure. Unstable, implausible behavior of the fusion filter or the stochastic model is the result.

It is therefore an object of the invention to propose an improved method for the correction of measurement data.

This object is achieved by the method for the correction of measurement data and / or navigation data of a sensor base system according to claim 1. The invention relates to a method for correcting measurement data and / or navigation data of a sensor-based system,

wherein the sensor base system and at least another Sen ^¬ sorsystem Measurement data acquisition, wherein the measurement data describe directly and / or indirectly navigation data, the navigation data from the measurement data and / or from known physical and / or mathematical relationships indirectly described are calculated, wherein the navigation data of at least a Speed, wherein by means of the measurement data and / or the navigation data of the at least one further sensor system a determination of error values of the measurement data and / or the navigation data of the sensor base system takes place, the error values being corrected by applying corrections,

wherein the sensor base system in vehicle-mounted or in

detected topocentric coordinates, wherein the at least one further sensor system also detected in vehicle-fixed or in topocentric coordinates and wherein the determination of the error values of at least the speed takes place in vehicle-fixed coordinates.

The use of vehicle-fixed coordinates according to the invention means that an orientation-independent attachment of speeds measured in vehicle-fixed coordinates becomes possible and the uncertainties of the measured data or navigation data are still correctly described. In addition, there is no limitation if only corrections measured in topocentric coordinates are available, since the sensor base system detects itself in vehicle-fixed coordinates and therefore the orientation angles between vehicle-fixed coordinates and topocentric coordinates can be determined. Thus, an independent, but also a simultaneous Ver ^¬ applicability of fixed to the vehicle measured and measured in topocentric corrections j edem operating state is reached. Even if it is expected in the case of unknown orientation angle with the Ge ^¬ speed in topocentric co-ordinates, is determined by the transformation back to the vehicle-fixed coordinate eliminates this error and thus obtain the correct speed in vehicle-fixed coordinates. The uncertainty of the orientation angles and the resulting increase in the uncertainty of the position is determined correctly and can be applied as a correction.

A particular advantage of the invention is thus in particular that error values for a speed can be determined better and more reliable and corrections for the speed or be ^¬ can be taken within more reliable.

Topocentric coordinates are understood to mean coordinates that correspond to a given surface, e.g. the whole world, are fixed.

Vehicle-fixed coordinates are understood to mean coordinates that always have the observer in their origin. In particular, the vehicle-fixed coordinates represent a Cartesian coordinate system.

The corrections preferably correspond to the negative error values. Preferably, the variances of the error values are also calculated.

According to a preferred embodiment of the invention, it is provided that the attachment of the corrections of the error values of at least the speed takes place in vehicle-fixed coordinates.

According to another preferred embodiment of the invention, it is provided that the topocentric coordinates are local topocentric coordinates aligned along the geographic directions.

According to a further preferred embodiment of the invention, it is provided that the sensor base system comprises an inertia navigation system is The inertial navigation system as a sensor base system has the advantage that it has the comparatively highest availability, since it has a ver ^¬ comparatively high output rate of the detected input data and also works largely independently of external disturbances.

According to a further preferred embodiment of the invention, it is provided that the at least one further sensor system is an odometry navigation system which detects the speed in vehicle-fixed coordinates. The

Odometry navigation system determines the speed e.g. About the known rolling circumference of the motor vehicle tires and thus enables a position determination taking into account the steering angle in the context of a dead reckoning.

According to a particularly preferred embodiment of the invention, it is provided that a second further sensor system is a global satellite navigation system which outputs the speed in topocentric coordinates. At the global

Satellite navigation system may be, for example, a so-called GPS navigation system, which detects the speed in earth-fixed Cartesian coordinates and converts them into topocentric coordinates in a multistage method known to those skilled in the art. It is particularly expedient that the satellite navigation system comprises at least two satellite signal receivers. Thus it improves the quality of the acquired satellite signals and thus the reliability and accuracy of the satellite navigation system.

According to a further preferred embodiment of the invention, it is provided that the navigation data further comprise at least one position and one orientation. This navigation information generally allows a comparatively good navigation guidance. In addition, a speed can also be included, which allows a further improved navigation guidance. According to another preferred embodiment of the invention, it is provided that the navigation data of the sensor base system and of the at least one further sensor system are merged to form a fusion data set. A common merger record is relative to the individual navigation information is generally reliable and accurate, and in particular it allows using an error estimate a ver ^¬ tively reliable assessment of the accuracy or reliability ^¬ to the merged input data or navigation information.

In accordance with a further preferred embodiment of the invention, it is provided that the error values are determined by means of an eror-state-space filter, in particular by means of an eror-state-space Kalman filter. In this case, the erorr State-space filter represents a fusion filter for the fusion of the output data or navigation information, in particular for the fusion of normally distributed output data or navigation information. At the same time estimates or preferably determines the error state-space filters the error values to ^¬ minimum of sensor-based system. By means of the at least one correction system, the error values and possibly also unknown variables of the inertial navigation system can then be estimated or determined. A special feature of ER- ror state-space filter, then, is to be estimated, or instead of Sen ^¬ sorsignale and the input data only error values domestic krementell determined and then corrected. The error values namely have a signi ficantly lower ^¬ temporal dynamics than the output data itself, whereby a significant decoupling of the dynamics of the ER- ror state-space filter on the characteristics of Sensorba ^¬ sissystems or the at least one correction system

is reached. According to a further preferred embodiment of the invention, it is provided that the corrections are added continuously. This results in the advantage that the respectively corrected output data or navigation information is simple can be further corrected if new corrections become necessary.

The invention further relates to a system for correcting measurement data and / or navigation data of a sensor base system comprising a sensor base system, at least one further sensor system and a fusion filter, wherein the sensor base system and the at least one further sensor system are designed to acquire measurement data.

wherein the measurement data directly and / or indirectly describe navigation data, wherein the fusion filter is configured to calculate indirectly described navigation data from the measurement data and / or from known physical and / or mathematical relationships, the navigation data comprising at least one speed, wherein the Fusion filter is adapted to make a determination of error values of the measured data and / or the navigation data of the sensor base system by means of the measurement data and / or the Na ^¬ vigationsdaten the at least one further sensor system, wherein the fusion filter is adapted to the error values by means of a mounting corrections to correct, wherein the sensor base system is adapted to detect in vehicle-fixed or in topocentric coordinates, wherein the at least one further sensor system is adapted to also detect in vehicle-fixed or in topocentric coordinates and wherein the Fusio nsfilter is designed to make the determination of the error values of at least the speed in vehicle-fixed coordinates. The system according to the invention thus comprises all devices necessary for carrying out the method according to the invention.

It is preferably provided that the system is designed to carry out the method according to the invention. This leads to the advantages already described.

Moreover, the invention relates to a use of the system according to the invention in a motor vehicle. ₁

Further preferred embodiments will become apparent from the subclaims and the following description of an embodiment with reference to figures. Show it

Fig. 1 by way of example a possible embodiment of a system according to the invention, which is designed for position ^¬ determination, in a motor vehicle and

2 shows by way of example a further possible embodiment of a system according to the invention, which is likewise designed for position determination, in a motor vehicle.

Fig. 1 shows a schematic representation of an embodiment of the system according to the invention, which is provided for arrangement and use in a motor vehicle (not shown). The illustrated system is designed, for example according to the positi ^¬ onsbestimmung of the motor vehicle. Here are all included by the system illustrated elements or components or sensor systems as function blocks and their together ^¬ menwirken shown with one another. The system includes inertial navigation system 101 configured to sense at least the accelerations along first, second, and third axes and at least the yaw rates about the first, second, and third axes. The first axis corresponds, for example according to the longitudinal axis of the motor vehicle, the second axis corresponds to the transverse axis of the motor vehicle and the third axis corresponds to the vertical axis of the motor vehicle. These three axes form a Cartesian coordinate system, the so-called motor vehicle coordinate system, which measures in vehicle-fixed coordinates.

Inertial navigation system 101 forms, for example, the so-called sensor-based system, the measured data of which are described below be corrected described further sensor systems or the so-called. Correction systems. The correction systems are Odometrienavigationssystem 103 and Satellitennavigationssys ^¬ tems 104th

The system also has a so-called.

Strapdown algorithm unit 102, in which a so-called. Strapdown algorithm is performed, by means of which the measurement data of inertial navigation system 101, among other things, in position data are converted. For this, the measurement data from inertial navigation system 101, which naturally describe Accelerat ^¬ fixing certificates, integrated twice over time. Also, an alignment of the motor vehicle is determined by means of simple Integ ^¬ ration of the respective measurement data of Trägheitsnavigati- onssystem 101 over time. By means of a single ^¬ integration over time, the orientation and speed of the motor vehicle can be further determined. In addition, strap-down algorithm unit 102 compensates for acting on Träg ^¬ integrated navigation system 101 Coriolis force.

The output data from strapdown algorithm unit 102 to ^¬ therefore consider the following physical quantities:

the speed, the acceleration and the rate of rotation of the motor vehicle, for example according to said three axes of the motor vehicle coordinate system and, for example, additionally in each case based on a topocentric Koordina ^¬ tensystem, which is suitable for describing the orientation or of dynamic variables of the motor vehicle in the world. By way of example, the said topocentric coordinate system is a GPS coordinate system. Additionally, the output data from strapdown algorithm unit 102 includes the position with respect to the vehicle coordinate system and the orientation with respect to the topocentric coordinate system. In addition, the output data of

Strapdown algorithm unit 102, the variances as information about the data quality of the above physical quantities or navigation information. For example, these variances are not included in strapdown algorithm unit 102. but only used by this and forwarded. The values calculated by the strapdown algorithm unit 102 og phy sical ^¬ sizes or navigation information is output via output module 112 and provided to other vehicle systems.

The system also includes odometry navigation system 103 in the form of wheel speed sensors for each wheel of the motor vehicle. For example, it is a four-wheeled motor vehicle with four wheel speed sensors, each detecting the speed of their associated wheel and its direction of rotation. Furthermore, odometry navigation system 103 comprises a steering angle sensor element which detects the steering angle of the motor vehicle. In addition, the exemplary system shown

Satellite navigation system 104, which is designed so that it determines the distance between each associated satellite and the vehicle and the speed between the associated satellite and the vehicle. In addition, satellite navigation system 104, for example according ^¬ fusion filter 105, a start position or start position information providing, or at least at the start. Turn on the system. The system also includes fusion filter 105. Fusion filter 105 provides a fusion data set 106 as the measurement data from the odometry navigation system 103, satellite navigation system 104, and inertial navigation system 101 are shared. Fusion data set 106 has the acquired measurement data of the different sensor systems, wherein fusion data set 106 includes, for example, additional error values and variances associated with the error values which describe the data quality. The measurement data of inertial navigation system 101 are now stored in a dedicated electronic data storage 113 of fusion filter 105 for a predetermined period of time. Inertial navigation system 101 represents the so-called sorbase system, while odometry navigation system 103 and satellite navigation system 104 represent the so-called correction systems whose measurement data or navigation information is used to correct the values of the sensor-based system, provided they have been made plausible. According to the example, the measured data or Navigation information of the sensor base system, ie the measurement data or navigation information of the inertial navigation system 101, stored for 25 measurement periods. If necessary, that is, if the stored measurement data was acquired at a different time than the measurement data or navigation ^¬ information of the correction systems, the required measurement data or navigation information is interpolated from the stored measurement data or navigation information. The measurement data or navigation information of the correction systems, ie of satellite navigation system 104 and of

Odometry navigation system 103, however, are not stored.

This ensures that measurement data or navigation information that was at least apparently acquired at an identical point in time can always be subjected to the comparison.

For example, fusion data set 106 provided by fusion filter 105 includes that which has been checked for plausibility

Measurement data or navigation information of the correction systems determined quantitative error of the sensor base system, by means of the measurement data or navigation information of the sensor base system.

Strapdown algorithm unit 102 is now corrected by Fu ^¬ sion data record 106, the measurement data or navigation information of the sensor base system. Fusion data set 106 is calculated by fusion filter 105 from the measurement data or navigation information from odometry navigation system 103, satellite navigation system 104 and inertial navigation system 101. By way of example, fusion filter 105 is embodied as an Errator-State-Space Kalman filter, that is to say as a Kalman filter, which in particular carries out a linearization of the measured data or navigation information and in which the quantitative error values of the measured data or navigation information are calculated or estimated and which operates se ^¬ quentiell and thereby the measurement data or values available in the respective functi ^¬ onsschritt the sequence. Fusion filter 105 is designed to always be asynchronously the most recent of inertial navigation system 101,

Odometrium navigation system 103 and satellite navigation system 104 available measured data or values. By way of example, the measured data or values are thereby routed via motor vehicle model unit 107 and alignment model unit 109.

The motor vehicle model unit 107 is configured to derive from the measurement data or navigation information of

At least the velocity along a first axis, the velocity along a second axis and the rate of rotation about a third axis calculates and provides these fusion filters 105.

The system also includes tire parameter estimation unit 110, which is designed such that it calculates at least the semia ^¬ knife, for example, the dynamic radius of all wheels and additionally calculates the slip stiffness and the slip stiffness of all wheels and this Kraft ^¬ vehicle model unit 107 beitstellt as additional input variables , Tire parameter estimation unit 110 is further configured to use a substantially linear Rei ^¬ fenmodell for calculating the tire sizes.

The example according to inputs from Reifenparameterschät- wetting unit 110 are the wheel speeds and the steering angle descriptive data, at least partially, the output values of the strapdown algorithm unit 102, as well as certain of fusion ^¬ filter 105 variances. The system also includes

GPS error detection and plausibility unit 111, which is designed such that, for example, it receives as input data the measurement data or navigation information from satellite navigation system 104 as well as at least partial measurement data or

Obtained navigation information from strapdown algorithm unit 102 and taken into account in their calculations.

GPS error detection and-plausibilization 111 examines the measurement data or navigation information to a tellitennavigationssystem to Sa- 104 adapted stochastic Mo ^¬ dell. If the measured data or navigation information corresponds to the model within the framework of a tolerance that takes account of the noise, then it is checked for plausibility. In this case, GPS error detection and plausibility check unit 111 is additionally connected to data-level fusion filter 105 and transmits the plausible measured data or navigation information to fusion filter 105. GPS error detection and plausibility check unit 111 is configured by way of example to provide a method for selecting a satellite inter alia by means of the following method steps:

Measuring position data of the motor vehicle relative to the satellite based on the sensor signals of satellite navigation system 104,

- determining redundant to the determined based on the sensor signals from the satellite navigation system 104 Positionsda ^¬ th reference position data of the motor vehicle, - selecting the satellite when a comparison of the position data and the reference position data of a predetermined condition is satisfied,

wherein a difference between the position data and the reference position data is formed in order to compare the position data and the reference position data,

wherein the predetermined condition is a maximum permissible deviation of the position data from the reference position data, - the maximum allowable deviation from a standard deviation depends ^¬, which is calculated based on a sum of a reference variance for the reference position data and a measurement variance for the position data, and

- Where the maximum allowable deviation corresponds to a multiple of the standard deviation such that a probability that the position data fall in a dependent of the Standardab ^¬ scattering interval falls below a predetermined threshold.

The system also has standstill detection unit 108, which is designed such that it can detect a stoppage of the motor vehicle and, in the event of a detected standstill of the motor vehicle, at least fusion filter 5 provides information from a standstill model. The

Information from a standstill model describes that the rotation rates around all three axes have the value zero and the velocities along all three axes have the value zero. In this case, the standstill detection unit 108 is designed in such a way that it uses as input data the sensor signals of the wheel speed sensors of

Odometry navigation system 103 and the sensor signals of inertial navigation system 101 uses. By way of example, the system uses a first group of measurement data or navigation information relating to a motor vehicle coordinate system, ie vehicle-fixed coordinates, and additionally a second group of measurement data or navigation information relating to a topocentric coordinate system. By determining the error values of the speed in vehicle-fixed coordinates according to the invention, it becomes possible for an orientation-independent attachment of corrections to speeds measured in vehicle-fixed coordinates to be possible, and the uncertainties of the measurement data or navigation data to continue to be correctly described. Moreover, there is no limitation if only corrections measured in topocentric coordinates are available, since the sensor base system itself is in vehicle-fixed coordinates detected and therefore the orientation angle between vehicle-fixed coordinates and

topocentric coordinates are determinable. Thus, an independent, but also a simultaneous, usability of vehicle-measured and in topocentrically measured corrections in each operating state is achieved. Even if it is expected in the case of unknown orientation angle with the Ge ^¬ speed in topocentric co-ordinates, is eliminated, this error by the transformation back to the vehicle-fixed coordinate and thus obtain the correct speed in the vehicle-fixed coordinates. The uncertainty of the orientation angles and the resulting increase in the uncertainty of the position is determined correctly and can be applied as a correction.

The particular orientation of the model unit 109, Reg ^¬ tung angle between the motor vehicle coordinate system and the topocentric coordinate system is determined on the basis of the following physical quantities:

- the vectorial speed with respect to the

topocentric coordinate system,

- the vectorial speed with respect to the

Motor vehicle coordinate system,

- the steering angle and

- the respective quantitative error of the said

Sizes describing measured data or values.

Alignment model unit 109 uses all of the output data from strapdown algorithm unit 102.

Alignment model unit 109 is, for example according formed so that they have an in ^¬ formation calculated in addition to the orientation angle on the quality of the orientation angle in the form of a variance and provides fusion filter 105th

Fusion filter 105 uses the orientation angle and the variance of the orientation angle in its calculations, which it passes via fusion data set 106 to strapdown algorithm unit 102.

Fusion filter 105 thus acquires the measurement data or navigation information from inertial navigation system 101, the

Sensor base system, and odometry navigation system 103 and satellite navigation system 104, the correction systems.

Fig. 2 shows an example of another possible embodiment of a system according to the invention, which is also designed for Posi ^¬ tion determination, in a motor vehicle (not shown). The system comprises, for example according to Trägheitsna ^¬ vigationssystem 201, GPS 204, and 203 Odometrienavigationssystem as different sensor systems. Inertial navigation system 201, Satellitennavigati ^¬ onssystem Odometrienavigationssystem 204 and 203 provide data that describe, directly or indirectly navigation data, namely a position, a speed, a Accelerat ^¬ nigung, an orientation, a yaw rate or a

Yaw acceleration, to fusion filter 205 off. The output of the measurement data or navigation information takes place via a vehicle data bus. ^{By way of} example, satellite navigation system 204 outputs its measurement data or navigation information in raw data form.

As a central element in a position determination of

Motor vehicle inertial navigation system 201, which is a so-called MEMS IMU

(Micro-Electro-Mechanical System Inertial Measurement Unit) is used in combination with strapdown algorithm module 207, since this is assumed to be error-free, ie, it is assumed that the measurement data or Navigationsinforma ^¬ tions of inertial navigation system 201 always According to their stochastic model, they only have noise effects and are thus free of external or random errors or disturbances. The noise as well as remaining non-modeled errors of inertial navigation system 201, such as non-linearity, are calculated over the measuring range as averaged, stationary and normally distributed (so-called Gaussian white noise) assumed.

Inertial navigation system 201 comprises three yaw rate sensors which mutually register orthogonally and three acceleration sensors which detect each other orthogonally in each case. Sa ^¬ tellitennavigationssystem 204 includes a GPS receiver, which initially carries out over the satellite signal propagation time Entfer ^¬ voltage measurements to the receivable GPS satellites and also stretch out the change of the satellite signal transit time as well as additionally from the change in the number of wavelengths of the satellite signals, a distance traveled by the vehicle path ^¬ certainly. Odometrienavigationssystem 203 includes depending ^¬ weils a wheel speed sensor at each wheel of the motor vehicle, and a Lenwinkelsensor. The wheel speed sensors each determine the Raddrehgschwindigkeit their associated wheel and the steering angle sensor determines the chosen steering angle. Inertial navigation system 201 outputs its measurement data or navigation information to preprocessing unit 206 of inertial navigation system 201. Pre-processing unit 206 now corrects the measurement data or navigation information by means of correction values, which pre-processing unit 206 receives from fusion filter 205. The thus corrected Ausga ^¬ bedaten or the navigation information described therein to be continued to the strapdown algorithm unit 207th

Strapdown algorithm module 207 now makes a position determination based on the corrected measurement data or navigation information from preprocessing unit 206. This Po ^¬ sitionsbestimmung is a so-called. Dead reckoning based on inertial navigation system 201. For this, the output from pre-processing unit 206, corrected navigation measurement data or information is continuously integrated over time, or added together. Strapdown algorithm module 207 further compensates for a Coriolis force acting on inertial navigation system 201, which affects the measurement data or Navigation information from inertial navigation system 201. To determine the position leads

Strapdown algorithm module 207 a two-fold integration of the measurement data captured by the inertial navigation system 201 or navigation information that describe accelerations over time. This allows an updating of a previously known position as well as an updating of a previously known orientation of the motor vehicle. To determine a speed or a rotation rate of the motor vehicle, strapdown algorithm module 207 performs a simple integration over time of the measurement data or navigation information acquired by inertial navigation system 201. Furthermore, strapdown algorithm module 207 also corrects the determined position by means of corresponding correction values of fusion filter 205. Fusion filter 205 in this example performs the

Correction only indirectly via strapdown algorithm module 207. As determined by the strapdown algorithm module 207 and corrected measurement data and navigation information, so the position, speed, acceleration, alignment, the rotation rate and the rotational acceleration of the motor ^¬ vehicle will now be made to output module 212, and of fusion filter 205 ,

The so-called strapdown algorithm module 207 performs.

Strapdown algorithm is computationally only slightly complex and can therefore be realized as a real-time capable sensor base system. It represents a procedure for integrating the measurement data or navigation information from inertial navigation system 201 into speed, orientation and position and contains no filtering, so that an approximately constant latency and group delay result.

The term sensor base system describes the one sensor system whose measurement data or navigation information is corrected by means of the measurement data or navigation information of the other sensor systems, the so-called correction systems. By way of example, the correction systems are Odometrienavigationssystem 203 and to satellite navigation system ^¬ 204th

Inertial navigation system 201, preprocessing unit 206 of inertial navigation system 201 and strapdown algorithm module 207 form, by way of example, the so-called sensor-based system, to which fractionally also fusion filter 205 is counted. Output module 212 relays the measured data or navigation information determined and corrected by strapdown algorithm module 207 to any other systems of the motor vehicle.

The measurement data acquired by satellite navigation system 204 are, for example, in the form of sensor signals via a so-called UART data connection, first forwarded to preprocessing unit 208 of satellite navigation system 204. Pre-processing unit 208 then determines from the output from satnav ^¬ gationssystem 204 measuring data representing raw GPS data, and also include a description of the orbit of each GPS signal transmitting GPS satellite, a position and a speed of the motor vehicle

GPS coordinate system. In addition, satellite navigation system 204 determines a relative speed of the motor vehicle to the GPS satellites from which GPS signals are received. Furthermore, preprocessing unit 208 corrects a time error of a receiver clock of satellite navigation system 204 caused by drift of the receiver clock contained in the measurement data, and by means of a correction model, the changes in the signal propagation time and the caused by atmospheric influences on the GPS signals transmitted by the GPS satellites pathway. The correction of the time error as well as the atmospheric influences take place by means of correction values obtained by fusion filter 205.

Satellite navigation system 204 is still approximately feasibility check module 209 associated with which the measurement data or Navigationsinforma- output by preprocessing unit 208 ^¬ tions, ie the position and the speed of the vehicle ^¬ , plausibilisiert. The plausibility data or navigation information plausibilized by plausibility module 209 are then output to fusion filter 205.

The system further comprises preprocessing unit 210 of odometry navigation system 203, which includes those of

Odometrienavigationssystem 203 receives recorded measurement data or navigation information. In this case, the acquired measurement data or navigation information are the measurement data or navigation information of the individual wheel speed sensors as well as the measurement data or navigation information of the steering angle sensor. Pre-processing unit 210 now determines the position and orientation of the motor vehicle in the motor vehicle ^coordinate system from the measured data or navigation information output by odometry navigation system 203 in accordance with a so-called coupling navigation ^method . Furthermore, the speed, the acceleration, the yaw rate and the

Spin acceleration of the motor vehicle determines, also in the motor vehicle coordinate system. In addition, pre-processing unit 210 corrects the measurement data or navigation information obtained by odometry navigation system 203 by means of correction values obtained by fusion filter 205. Odometrienavigationssystem 203 is associated with further plausibility check 211 which plausibility check the measurement data output from preprocessing ^¬ unit 210 or navigation Informa ^¬ functions, that is, the position, orientation, velocity, acceleration, the rate of rotation and the rotational acceleration of the motor vehicle. Since the error values of

Measurement data or navigation information from

Odometrienavigationssystem often random 203, environmental disturbances that do not correspond white noise, for example when a comparatively large wheel slip are used, the integrated navigation system by means of inertia 201, and by means Satellitennavigati ^¬ onssystem 204 specific measurement data or navigation information to the measured data and navigation information from Odometrienavigationssystem 203 to make it plausible. To- Next, however, the measured data or navigation information is compared with a sensor-specific model assigned to it, which takes into account measurement uncertainties such as noise influences. If the measurement data or navigation information corresponds to the model within the given limit values or tolerance ranges, one takes place here

Plausibilization and the plausibility-related measurement data or navigation information are further processed. The plausibility measurement data or navigation information is then forwarded to fusion filter 205. If a plausibility check of these measurement data or navigation information can not take place, the corresponding measurement data or navigation information is rejected and not further processed.

Fusion filter 205 is embodied, for example, as an Eror State Space Kalman filter. The main task of fusion filter 205 is such as, measurement data or Na ^¬ vigationsinformationen the sensor base system, ie from

Inertial navigation system 201, by means of measurement data or

To correct navigation information from Odometrienavigationssystem 203 and satellite navigation system 204, which represent the correction systems, or output corresponding correction values to strapdown algorithm module 207. Since inertial navigation system 201, for example, as free of random

Error and external interference is assumed, the measurement data or navigation information from inertial navigation system 201 subject exclusively to white noise. Due to the different signal output delays of inertial navigation system 201, odometry navigation system 203 and satellite navigation system 204, the measurement data or navigation information from inertial navigation system 201 is stored over a period of 25 measurement periods in an electronic data memory, not shown. It is thus ensured that measured data or navigation information from inertial navigation system 201 is always used for the measurement data or navigation information from odometry navigation system 203 and also from navigation satellite system 204 There are comparisons that have been recorded, for example, at an identical point in time.

Since it is in merger filter 205 a so-called. ER- ror state-space Kalman filter, the quantitative error values of the measured data and navigation information is exclusively intended and performed appropriate corrections ^¬. This simplifies and accelerates the merger of the measurement data or navigation information of inertial navigation system 201, which was carried out by fusion filter 205,

Odometry navigation system 203 and satellite navigation system 204 to a common fusion data set. Thus, a real-time capable position determination and correction of the position determination is possible.

The system shown in FIG. 2 represents a so-called virtual sensor, wherein inertial navigation system 201,

Odometry navigation system 203 and satellite navigation system 204, however, are not components of the virtual sensor. A virtual sensor is a system which, regardless of the type of integrated sensor systems - always produces the same output data or outputs - here so Trägheitsnaviga ^¬ tion system 201, Odometrienavigationssystem 203 and satellite navigation system 204th On the basis of the output data or outputs is not clear what sensor systems are powered ^¬ connected into the system.

The system exemplified in FIG. 2 also uses a first group of measurement data or navigation information relating to a motor vehicle coordinate system, ie vehicle-fixed coordinates, and additionally a second group of measurement data or navigation information relating to a topocentric coordinate system, ie topocentric Coordinates, refer. The measurement data or navigation information relating to the motor vehicle coordinate system is measurement data or navigation information of the inertial navigation system 201 and of the measurement data or navigation information relating to the vehicle The determination according to the invention of the error values of the velocity in vehicle-fixed coordinates makes it possible for an orientation-independent application of corrections to speeds measured in vehicle-fixed coordinates to be made possible and the uncertainties of the Measurement data or navigation data of inertial navigation system 201 will continue to be described correctly. The result is furthermore no limitation if only available in topocentric coordinates measured corrections, since the sensor base system detected even in the vehicle-fixed coordinates and hence the angle of orientation between the vehicle-fixed Koordi ^¬ naten and topocentric coordinates are determined. Thus, an independent, but also a simultaneous

Availability of vehicle-measured measured and measured in topocentric corrections achieved in each operating state. Even if it is expected in the case of unknown orientation angle with the Ge ^¬ speed in topocentric co-ordinates, is eliminated, this error by the transformation back to the vehicle-fixed coordinate and thus obtain the correct speed in the vehicle-fixed coordinates. The uncertainty of the orientation angles and the resulting increase in the uncertainty of the position is determined correctly and can be applied as a correction.

According to another exemplary embodiment, which is not illustrated, the fusion filter determines the states or physical variables alignment errors, velocity errors, position errors, intercept errors, gyroscopes, intercept errors, accelerometers, scale factor errors

Gyroscopes, scale factor error accelerometer,

GPS receiver clock error and GPS receiver clock error drift. The intersection error is also known as so-called offset error. The term accelerometer refers to acceleration sensors. Inertial sensors. The following table shows by way of example an overview of error values determined by the fusion filter, their commonly used symbolism, the commonly used measuring unit as well as the commonly used coordinate system:

This results, by way of example, in the resulting state vector:

^XX

Definitions:

true alignment matrix (direction cosine matrix) between navigation and vehicle-fixed coordinates

Positional error between true and estimated

direction cosine

estimated direction cosine matrix between navigation and vehicle fixed coordinates

By way of example:

The cross-product-forming matrix [p x] of a 3x1 vector p is defined as:

with the property:

Thus, by way of example, the following system model can be created, in which the basic equations known per se are multiplied by the direction cosine matrix or its

Transposed are transformed into the vehicle-fixed coordinate system:

Definition / substitution

Alignment error: Speed error:

Position error:

System model:

Assumption: independence of the GPS clock error states from the other errors.

Partial derivative: alignment error rate by rotation rate:

Partial derivative: alignment error rate to byte velocity:

Partial derivative: alignment error rate by position:

Partial derivative: alignment ^{error rate} according to axial section ^{error of} the gyroscope:

Partial derivative: alignment ^{error rate} according to axial section ^{error of} the accelerometer:

Partial derivative: alignment error rate after

Scale factor error of the gyroscopes: ς _ύ

Partial derivative: alignment error rate after

Scale Factor Error of Accelerometer:

Partial derivative: Velocity error rate according to rotation rates:

Partial derivative: Velocity error rate to byte velocity:

Partial derivative: Speed error rate by position

Partial derivative: velocity ^error rate on axes ^¬ section errors of the gyroscopes:

Partial derivative: velocity ^error rate by axis ^¬ section error of the accelerometer:

Partial derivative: Speed error rate after

Scale factor error of the gyroscopes:

Partial derivative: Speed error rate after

Scale Factor Error of Accelerometer:

Partial derivative: Position error rate according to rotation rate

Partial derivative: Position error rate to byte velocity:

Partial derivative: Position error rate by position:

Partial derivative: position ^{error rate} according to axial section ^{error of} the gyroscope:

Partial derivative: position ^{error rate} according to axial section ^{error of} the accelerometer:

Partial derivative: position error rate after

Scale Factor Error of Accelerometer:

Partial derivative: position error rate after

Scale factor error of the gyroscopes:

Model of GPS receiver clock error and its drift:

Partial derivation: tire radius:

Resulting system model:

Furthermore, a model for navigating by means of determining the GPS code will be described below by way of example.

The great advantage of the code measurement is quick and un ^¬ complicated calculation which is why it is especially used in the navigation of the position. The distance is calculated using the "law of kinematics Newton 'see'. Path = Ge ^¬ speed x time, the speed at which the radio signal propagates is the speed of light, or about 290.00 km in the second order for the receiver. To determine the time of the signal travel, he must know when the radio signal left the satellite, the signal being modulated by two codes, the C / A code and the P code The satellite sends the C / A signal with help the highly accurate atomic clocks. the receiver also has a clock with which it can generate its own C / a code. the receiver now shifts both codes (received and self-produced code) until they are de ^¬ congruent addition, they must each other.

Be correlated. The C / A code is a pseudorandom digital code. In reality, it is repeated a thousand times per second. In this way, the receiver can determine the duration of the signal travel.

Measurement Equation for GPS Code:

where z _{PSR is} the pseudo orange measured by the GPS and z _{PSR is} the computational pseudo orange from the strapdown algorithm: The so-called pseudoranges describe distances, which are used for location determination. They deviate from the true distances by a constant, but for the time being unknown amount. First, therefore, the duration of the radio signals from the satellite used to the receiver of the observer measured. This results in the current distances to the satellites, which are still associated with errors of the clocks (satellite, receiver) and other influences. However, if the satellite clocks are exactly synchronized with each other, then all measured transit times are practically only affected by the synchronization error of the receiver clock - ie all falsified by almost the same amount. These distances, which are too long or too short by a constant, are called pseudo ranges (pseudo ranges).

With

Position of the satellite in navigation coordinates

Lever arm from IMU (coordinate origin Nav coordinates) to

GPS antenna (phase center)

Receiver clock error, already converted over the speed of light to a distance

Further, the unit vector in navigation coordinates pointing from the antenna toward the respective satellite is defined as:

Measuring model alignment error for GPS

Measuring model speed error for GPS code measurement:

Measuring model position error for GPS code measurement:

Measuring model offset gyroscopes for GPS code measurement:

Measuring model offset accelerometer for GPS code measurement:

Measuring model scale factor error gyroscopes for GPS code measurement:

Measuring model scale factor error Accelerometer for GPS code measurement:

Measuring model receiver clock error and drift for GPS code measurement:

Measuring model tire radius for GPS code measurement:

.1)

Resulting observation matrix for GPS code measurements: Further, by way of example a model for Na ^¬ vigieren means of determining differentiated

GPS carrier phases are described.

The carrier phase measurement is a purely geodetic method with which a very high resolution accuracy in the millimeter range can be achieved. This measurement requires a high-quality receiver, which can measure at least the carrier phase LI and possibly also the carrier phase L2. Compared to code measurement, the carrier phase measurement is much more complex and time-intensive. In this observation, not the codes, but the carrier waves are compared. By determining the phase ambiguity, the number of whole waves between satellite and receiver can be determined. The wavelength of the LI Signal is 19.05 cm and the L2 signal is 24.45. But since the signal does not arrive at the receiver with a whole wavelength, the length of this phase remainder piece still has to be determined. Geodetic receivers can do this down to the millimeter.

Measurement equation for GPS carrier phase: Here, z _{DPH is} the differential velocity measured by the GPS to the satellite and z _{DPH is} the differential speed calculated from the strapdown algorithm:

With

Speed of the satellite navigation coordinates receiver clock error drift, converted already have the Lichtgeschwin ^¬ speed to a speed

Measurement model alignment error for GPS carrier phase measurement:

Measuring model velocity error for GPS carrier phase measurement:

Measurement model position error for GPS carrier phase measurement:

Measuring model Gyroscope intersection error for GPS carrier phase measurement: Measurement model intercept error of accelerometer for GPS carrier phase measurement:

Measuring model scale factor error of gyroscopes for GPS carrier phase measurement:

Measuring model scale factor error of the accelerometer for GPS carrier phase measurement:

Measurement model receiver clock error and drift for GPS Trägerpha ^¬ senmessung:

Measuring model tire radius for GPS code measurement:

Resulting observation matrix for GPS carrier phase measurements:

Furthermore, a model for navigating by means of odometry is described below by way of example. Measuring equation for odometry: this is the measured by the odometry movement ^¬ speed of RadaufStandspunktes in vehicle-fixed coordinates and the calculated speed of movement from the strapdown algorithm: M

To form the partial differentials of the measurement equations to correct both the sizes of the strapdown algorithm as well as the tire radii become the approximate sizes and both in the prediction vector united, and the measurement vector z _{odo set} to zero:

This results in all partial differentials from z _odo , insertion in

gives:

With

measured angular velocity of the wheel

Rotating matrix by the angle between body coordinates and wheel coordinates

Lever between IMU (coordinate origin) and

wheel contact

dynamic tire radius

Measuring model alignment error for odometry measurements:

Measuring model Speed error for odometry measurements: Measuring model position error for odometry measurements:

Measuring model Axis section error of the gyroscopes for

Odometry measurements:

Measuring model Axis error of the accelerometers for odometry measurements:

Measuring model scale factor error of the gyroscopes for

Odometry measurements:

Measuring model scale factor error of the accelerometers for odometry measurements:

Measuring model receiver clock error and drift for

Odometry measurements:

Measuring model Tire radius for odometry measurements

Resulting observation matrix for odometry:

Claims

claims

1. A method for correcting measurement data and / or navigation data of a sensor base system, wherein the sensor base system and at least one further sensor system acquire measurement data, wherein the measurement data directly and / or indirectly describe navigation data, wherein indirectly described navigation data from the measurement data and / or from known physical and / or mathematical relationships are calculated, wherein the navigation data comprise at least one speed, wherein by means of the measurement data and / or the navigation data of the at least one further sensor system, a determination of error values of the measurement data and / or the navigation data of the sensor base system, wherein the error values by means of an attachment are corrected by correction, wherein the sensor system detects in base fixed to the vehicle or in topocentric co-ordinates, and wherein the at least one further Sen ^¬ sorsystem also recorded in vehicle-fixed or in topocentric co-ordinates,

characterized in that the determination of the error values of at least the speed takes place in vehicle-fixed coordinates.

2. The method according to claim 1,

characterized in that the affixing of the corrections of the error values of at least the speed takes place in vehicle-fixed coordinates.

3. The method according to at least one of claims 1 and 2, characterized in that the topocentric coordinates are local topocentric coordinates, which are aligned along the geo ^¬ graphic directions.

4. The method according to at least one of claims 1 to 3, characterized in that the sensor base system (101, 201) is an inertial navigation system (101, 201).

5. The method according to at least one of claims 1 to 4, characterized in that the at least one further sensor system is an odometry navigation system (103, 203) detecting the speed in vehicle-fixed coordinates.

6. The method according to claim 5,

characterized in that a second further sensor system is a global satellite navigation system (104, 204) outputting the velocity in topocentric coordinates.

7. The method according to at least one of claims 1 to 6, characterized in that the navigation data further comprise at least one position and an orientation.

8. The method according to at least one of claims 1 to 7, characterized in that the navigation data of the sensor base system and the at least one further sensor system are fused to a fusion data set.

9. The method according to at least one of claims 1 to 8, characterized in that the error values by means of an error-state-space filter, in particular by means of an Eror ror-space-Kalman filter (105, 205), are determined ,

10. The method according to at least one of claims 1 to 9, characterized in that the corrections are added continuously.

11. system for correcting measurement data and / or navigation data of a sensor-based system,

comprising a sensor base system, at least one further sensor system and a fusion filter, wherein the sensor base system and the at least one further sensor system are configured to acquire measurement data,

wherein the measurement data directly and / or indirectly describe navigation data, wherein the fusion filter is designed to calculate indirectly described navigation data from the measurement data and / or from known physical and / or mathematical relationships, wherein the navigation data is min. least comprise a speed, wherein the fusion filter is adapted to make a determination of error values of the measured data and / or the navigation data of the sensor base system by means of the measurement data and / or the Na ^¬ vigationsdaten the at least one further sensor system, wherein the fusion filter is adapted to the Correcting error values by applying corrections, wherein the sensor base system is adapted to detect in vehicle-fixed or in topocentric coordinates and wherein the at least one further sensor system is adapted to also detect in vehicle-fixed or in topocentric coordinates,

characterized in that the fusion filter is adapted to make the determination of the error values of at least the Geschwin ^¬ speed in vehicle-fixed coordinates.

12. System according to claim 11,

characterized in that the system is designed to perform a method according to at least one of claims 1 to 10 from ^¬ .

13. Use of the system according to at least one of claims 11 and 12 in a motor vehicle.