KR20230065549A - Calibration method of inertial measurement unit - Google Patents

Abstract

In one embodiment of the present invention for solving the above problems, a calibration method of an inertial measurement device is disclosed. The method includes: acquiring GPS information and IMU attitude information; obtaining GPS attitude information based on the GPS information; whether or not the IMU attitude information is calibrated based on a comparison between the GPS attitude information and the IMU attitude information; and performing correction on the IMU posture information based on the determination result.

Description

Calibration method of inertial measurement device {CALIBRATION METHOD OF INERTIAL MEASUREMENT UNIT}

Various embodiments of the present disclosure relate to an inertial measurement unit (IMU), and more particularly, to a method for calibrating the inertial measurement unit.

In general, a vehicle refers to a transportation device that travels on a road or a track using fossil fuel, electricity, or the like as a power source. Recently, in order to reduce the driver's burden and enhance convenience, vehicles equipped with advanced driver assistance systems (ADAS) that actively provide information on the driving environment, such as the vehicle condition, driver condition, and surrounding environment, Research on this is actively progressing.

An advanced driver assistance system may include a sensing unit to sense a driving environment, and an example of the sensing unit may include an inertial measuring unit (IMU). The inertial measurement device may be provided in a mobile device (eg, a vehicle), and may be configured through a gyroscope, an acceleration sensor, and a geomagnetic sensor that measure free movement in a three-dimensional space. Such an inertial measurement device is recognized as a very important device in autonomous driving. A cornerstone of autonomous vehicle design is the ability to accurately sense the vehicle's position and trajectory at all times while driving. At this time, the automobile designer's task lies in 'always'. For example, GPS provides generally accurate information based on a vast installed base, but the satellite signals on which GPS depends are not 100% guaranteed, and signals may disappear in cities with high-rise buildings, bad weather, or tunnels. This can cause fatal results in vehicle control and positioning in autonomous driving beyond simply inconvenient navigation. To solve this problem, not only GPS but also on-board technologies such as LiDAR are applied together, and they have several advantages over GPS, but there is still a problem of being easily confused in complex situations such as congested intersections.

On the other hand, since the inertial measurement device can detect information about the attitude of the mobile device, it is considered very important in the field of autonomous driving technology. However, since the inertial measurement device operates not as an absolute value but as a relative value, it is necessary to calibrate to set an accurate reference point. In general, since the calibration of the inertial measurement device is performed using a separate device in a specific state of performing calibration, when the reference point of the inertial measurement device is operated in an incorrect state, the error of the inertial measurement device is not corrected. As the operation continues, the risk of accidents is high, which can cause safety problems.

Republic of Korea Patent Publication No. 10-2020-0140003

An object of the present invention is to provide a method for calibrating an inertial measurement device.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A calibration method of an inertial measurement device according to an embodiment of the present invention for solving the above problems is disclosed. The method includes: acquiring IMU attitude information and GPS information; acquiring GPS attitude information based on the GPS information; whether or not the IMU attitude information is calibrated based on a comparison between the GPS attitude information and the IMU attitude information; and performing correction on the IMU posture information based on the determination result.

In an alternative embodiment, the GPS information is obtained through a GPS device included in at least one of an inertial measurement device and a mobile device equipped with the inertial measurement device, and the IMU attitude information includes pitch ), one or more IMU attitude sub-information related to each of roll and yaw.

In an alternative embodiment, the GPS device may be provided in plurality, and each of the plurality of GPS devices may be provided at a position corresponding to each other in the mobile device.

In an alternative embodiment, the GPS information includes a plurality of pieces of GPS sub-information obtained from each of the plurality of GPS devices, and obtaining GPS attitude information based on the GPS information includes the plurality of pieces of GPS sub-information. and acquiring the GPS attitude information including one or more GPS attitude sub-information based on at least one of the above.

In an alternative embodiment, the acquiring of the GPS attitude information may include acquiring first GPS attitude sub-information based on a variation amount of the GPS information.

In an alternative embodiment, the obtaining of the GPS position information may include the first and second GPS sub-information and the second GPS sub-information obtained through the first and second GPS devices provided to correspond to each other on the left and right sides of the mobile device. obtaining 2GPS attitude sub-information based on the 3GPS position and the 3GPS position based on the 3GPS sub-information and the 4GPS sub-information obtained through a 3GPS device and a 4GPS device provided to correspond to the front and rear of the mobile device, respectively. It may include acquiring attitude sub-information.

In an alternative embodiment, the determining whether or not to correct the correction may include determining whether or not to correct the IMU attitude information based on whether the IMU attitude sub-information related to the pitch and the 1st GPS attitude sub-information match, and the IMU attitude related to the roll. Determining whether the IMU attitude information is corrected through whether the sub-information matches the 2nd GPS attitude sub-information, and determining whether the IMU attitude information is corrected through whether the IMU attitude-related sub-information related to yaw and the 3rd GPS attitude sub-information match. At least one of the determining steps may be included.

In an alternative embodiment, the method may include obtaining one or more nearby GPS information related to a nearby mobile device having the same traveling direction based on the GPS information, and obtaining GPS attitude prediction information based on the one or more nearby GPS information. The method may further include obtaining and determining whether to calibrate the IMU attitude information based on a comparison between the GPS attitude prediction information and the IMU attitude information.

In an alternative embodiment, determining whether to calibrate the IMU attitude information may include calculating a reliability of the GPS attitude information based on a comparison between the GPS attitude prediction information and the GPS attitude information, and the GPS attitude information. and determining whether or not to calibrate the IMU attitude information based on whether the reliability of the IMU attitude is greater than or equal to a threshold reliability.

In an alternative embodiment, the method may further include obtaining latest attitude information and determining whether to calibrate the IMU attitude information based on a comparison between the latest attitude information and the IMU attitude information.

According to another embodiment of the present invention, an inertial measurement device for performing calibration is disclosed. The inertial measurement device may include a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory, and the processor may perform the calibration method described above by executing the one or more instructions. .

According to another embodiment of the present invention, a computer program stored in a computer-readable recording medium is disclosed. The computer program may be combined with a computer that is hardware to perform a calibration method of the inertial measurement device.

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present disclosure, an inertial measurement device having improved accuracy through calibration may be provided.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a diagram schematically illustrating a system for performing a calibration method of an inertial measurement device according to an embodiment of the present invention.
2 shows an exemplary block diagram of an inertial measurement device for performing calibration related to an embodiment of the present invention.
3 shows an exemplary flowchart of a calibration method of an inertial measurement device related to an embodiment of the present invention.
4 is an exemplary view for explaining attitude information of a mobile device equipped with an inertial measurement device related to an embodiment of the present invention.
5 shows an exemplary flow chart of an inertial measurement device calibration method related to another embodiment of the present invention.
6 shows an exemplary flowchart of a method for performing calibration of an inertial measurement device based on reliability of GPS attitude information related to an embodiment of the present invention.
7 shows an exemplary flowchart of a calibration method of an inertial measurement device related to another embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into smaller numbers of components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means any kind of hardware device including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a computer may be understood as including a smartphone, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device, but is not limited thereto.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least a part of each step may be performed in different devices according to embodiments.

1 is a diagram schematically illustrating a system for performing a calibration method of an inertial measurement device according to an embodiment of the present invention. As shown in FIG. 1, a system for performing a calibration method of an inertial measurement device according to an embodiment of the present invention includes a mobile device 10, an inertial measurement device 100, a GPS device 200, and a user terminal (20) and networks. Here, the system for performing the calibration method of the inertial measurement device shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and can be added, changed, or added as needed. may be deleted.

According to one embodiment of the present invention, the moving device 10 relates to a moving body that moves by generating power, and may refer to a device equipped with the inertial measuring device 100 . For example, the mobile device 10 may include a vehicle, a drone, an air vehicle, a motorcycle, a tank, and the like, as shown in FIG. 1 . However, it is not limited thereto, and may further include various moving devices capable of moving by generating power.

In embodiments, the mobile device 10 may be an autonomous driving capable device. A device capable of autonomous driving may refer to a device capable of unmanned driving through communication with the outside without a user inside the mobile device 10 . For example, a device capable of autonomous driving may determine a movement situation by itself, but may drive in relation to a command or driving signal sent from the outside.

Such a mobile device 10 may be characterized by having the inertial measuring device 100 to obtain attitude information related to autonomous driving and to drive based on the attitude information. For example, the inertial measuring device 100 may be installed in a mobile device (eg, a vehicle) to obtain attitude information related to a position and a trajectory while the vehicle is driving. Since the mobile device 10 autonomously moves or travels based on the attitude information obtained through the inertial measurement device 100, acquiring accurate attitude information through the corresponding inertial measurement device 100 may be very important. For example, when a problem occurs in an inertial measurement device provided in a drone, it is not easy to control the attitude, so that the drone tilts or cannot maintain an upright attitude, so stable flight may be impossible.

However, since the inertial measurement device 100 operates not as an absolute value, but as a relative value according to movement, there is a risk of error in calculating attitude information. Even if a minute error occurs in calculating the attitude information, it may result in a great danger to driving. Accordingly, in order for the inertial measurement device 100 to calculate attitude information with improved reliability, calibration of setting an accurate reference point may be important.

In general, as the inertial measurement device 100 is configured through a 3-axis accelerometer and a 3-axis angular velocity, acceleration in the traveling direction, lateral direction, and height direction and roll, pitch, and yaw angular velocity can be measured. In addition, the inertial measurement device may be able to calculate the speed and attitude angle of the mobile device 10 by integrating the acceleration and the angular velocity.

Since the inertial measuring device 100 calculates attitude information based on the measured relative measured values (or sensed values) of each of the accelerometer and the gyroscope according to movement, errors may occur during the continuous movement process, and such errors These may act as a factor that deteriorates stability during the driving process of the mobile device.

Accordingly, the inertial measurement device 100 of the present invention can provide a calibration method for correcting an error generated by the device based on an accurate reference point. Specifically, the inertial measurement device 100 may perform calibration for correcting an error of the inertial measurement device 100 based on a specific waypoint. Here, information about a specific correction point may be based on GPS information.

According to one embodiment, a system for performing a calibration method of an inertial measurement device may include the GPS device 200 . The GPS device 200 may be provided in at least one of the mobile device 10 and the inertial measurement device 100 . The GPS device may refer to a device that obtains GPS attitude information that is a basis for calculating information on a specific calibration point that is a reference for calibration of the inertial measurement device 100 . Specifically, the GPS device 200 may refer to a device that acquires location information (ie, GPS information) through communication with an artificial satellite. The GPS device 200 is a satellite system that transmits microwaves through a special frequency, and may refer to a system or device that generates location information from information acquired based on a signal received from a satellite. For example, the GPS device may obtain location information by calculating a distance by calculating a time of arrival (TOA) for a signal transmitted from a satellite to reach a receiving device based on a signal received from the satellite. Here, the location information or GPS information may include information obtained by measuring positioning values of the mobile device 10 and expressing the corresponding positioning values in the form of latitude and longitude values. That is, the GPS device 200 may refer to a device capable of obtaining GPS information through communication with a satellite.

In an embodiment, the inertial measurement device 100 may perform calibration of the corresponding inertial measurement device 100 based on GPS information acquired by the GPS device 200 . That is, the inertial measurement device 100 of the present invention includes attitude information (eg, GPS attitude information) derived through GPS information obtained corresponding to the mobile device 10 and attitude information derived from the inertial measurement device 100. (eg, IMU attitude information) may be compared to perform error correction, that is, calibration. A calibration method of the inertial measuring device will be described later in more detail with reference to FIGS. 3 to 7 .

In various embodiments, the inertial measurement device 100 and the mobile device 10 may be connected to the user terminal 20 through a network, and determine the driving of the mobile device 10 in response to a user's movement control command, Alternatively, posture information of the mobile device 10 may be acquired.

Here, the network may refer to a connection structure capable of exchanging information between nodes such as a plurality of user terminals and devices. For example, the network includes a local area network (LAN), a wide area network (WAN), a world wide web (WWW), a wired and wireless data communication network, a telephone network, a wired and wireless television communication network, and the like.

In addition, here, the wireless data communication networks are 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi (Wi-Fi) Fi), Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, A Near-Field Communication (NFC) network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, and the like are included, but are not limited thereto.

In one embodiment, the user terminal 20 may be connected to the inertial measurement device 100 or the mobile device 10 through a network, and may provide a movement command control signal for moving the mobile device 10, In response to the transmitted movement command control signal, attitude information of the mobile device 10 that changes as the mobile device 10 moves may be provided.

Here, the user terminal is a wireless communication device that ensures portability and mobility, and includes navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA ( Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartphone, It may include all types of handheld-based wireless communication devices such as smartpads and tablet PCs, but is not limited thereto. For example, the user terminal may further include an artificial intelligence (AI) speaker and an artificial intelligence TV that provide various functions such as music appreciation and information search through interaction with a user based on a hot word.

Hereinafter, the calibration method of the inertial measurement device will be described in more detail with reference to FIGS. 2 to 7 .

2 shows an exemplary block diagram of an inertial measurement device for performing calibration related to an embodiment of the present invention.

As shown in FIG. 2 , the inertial measurement device 100 may include a network unit 110 , a memory 120 , a sensor unit 130 and a processor 140 . The components included in the aforementioned inertial measurement device 100 are exemplary, and the scope of the present invention is not limited to the aforementioned components. That is, additional components may be included or some of the above components may be omitted according to implementation aspects of the embodiments of the present invention.

According to an embodiment of the present invention, the inertial measurement device 100 may include a GPS device 200, a mobile device 10, and a network unit 110 that transmits and receives data with the user terminal 20. The network unit 110 may transmit and receive data for performing a calibration method of an inertial measurement device according to an embodiment of the present invention to and from other computing devices and servers. That is, the network unit 110 may provide a communication function between the inertial measurement device 100 and user terminals or between the inertial measurement device 100 and the mobile device 10 . For example, the network unit 110 may receive GPS information from the GPS device 200 . Also, for example, the network unit 110 may transmit information about the attitude of the mobile device 10 (eg, IMU attitude information) to the user terminal 20 or the mobile device 10 . Additionally, the network unit 110 allows information transfer between the inertial measurement device 100 and user terminals or between the inertial measurement device 100 and the GPS device 200 by calling a procedure to the inertial measurement device 100. can do.

The network unit 110 according to an embodiment of the present invention includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( Various wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) may be used.

In addition, the network unit 110 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), SC-FDMA ( Single Carrier-FDMA) and other systems.

In the present invention, the network unit 110 may be configured regardless of its communication mode, such as wired and wireless, and may be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). can In addition, the network may be the known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. The techniques described herein may be used in the networks mentioned above as well as other networks.

According to an embodiment, a location information module may be embedded in the network unit 110 of the inertial measurement device 100. The location information module is a module for obtaining the location (or current location) of the inertial measurement device 100, and a representative example thereof is a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, the inertial measurement device 100 may acquire the location of the inertial measurement device using a signal transmitted from a GPS satellite by using a GPS module. In this case, the GPS module may perform the same operation as the GPS device 200 of the present invention.

As another example, when the inertial measurement device 100 utilizes a Wi-Fi module, the inertial measurement device 100 is based on information of a Wi-Fi module and a wireless access point (AP) that transmits or receives a wireless signal. position can be obtained. If necessary, the location information module may obtain data on the location of the inertial measurement device 100 in substitution or addition. The location information module is a module used to acquire the position (or current position) of the inertial measurement device 100, and is not limited to a module that directly calculates or acquires the position of the inertial measurement device 100.

According to an embodiment of the present invention, the inertial measuring device 100 may include a memory 120 . The memory 120 may store a plurality of application programs driven by the inertial measurement device 100, data for estimating the posture of the mobile device 10, and commands. At least some of these applications may be present in each inertial measurement device from the factory.

According to an embodiment, the memory 120 may store a computer program for performing a calibration method of an inertial measuring device according to an embodiment of the present invention, and the stored computer program may be read and driven by the processor 140. can In addition, the memory 120 may store any type of information generated or determined by the processor 140 and any type of information received by the network unit 110 . For example, the memory 120 may temporarily or permanently store input/output data. For example, the memory 120 may store sensing values related to driving or information about errors due to comparison between IMU attitude information and GPS attitude information. The detailed description of the information stored in the aforementioned memory is only an example, and the present invention is not limited thereto.

According to an embodiment of the present invention, the memory 120 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium.

According to one embodiment of the present invention, the inertial measurement device 100 may include a sensor unit 130 . The sensor unit 130 may obtain sensing information that is a basis for acquiring attitude information (ie, IMU attitude information) of the inertial measurement device 100 . In an embodiment, the inertial measurement device 100 is provided in one area of the mobile device 10, and accordingly, the sensor unit 130 acquires sensing information that is a basis for obtaining attitude information of the mobile device 10. can That is, the sensor unit 130 may obtain sensing information based on calculation of IMU attitude information corresponding to the movement of the mobile device 10 .

According to the embodiment, the sensor unit 130 may include an acceleration sensor module and an angular velocity sensor module. The sensor unit 130 may sense information about accelerations in the forward, lateral, and height directions, and roll, pitch, and yaw angular velocities according to the movement of the mobile device 10. there is. For example, the sensor unit 130 may include a gyroscope, an acceleration sensor, a geomagnetic sensor, a barometer, and an altimeter. A gyroscope measures angular velocity (rad/s) and can measure how many degrees per time it has rotated, while an accelerometer measures acceleration and decomposes the gravitational acceleration when computing an initial value to determine how much it has tilted. It may be for estimating a posture by measuring or integrating acceleration and angular velocity according to movement. In addition, the geomagnetic sensor measures the strength of the magnetic flux based on the magnetic north by measuring the magnetism, so that the posture can be estimated by how much the sensor is twisted based on the magnetic north. The various sensors included in the above-described sensor unit and specific descriptions related to the role of each sensor are only examples, and the present invention is not limited thereto.

According to one embodiment of the present invention, the inertial measurement device 100 may include a processor 140 . The processor 140 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of the computing device. unit) may include a processor for data analysis.

According to one embodiment, the processor 140 may typically process overall operations of the inertial measurement device 100 . The processor 140 processes signals, data, information, etc. input or output through the components described above or runs an application program stored in the memory 120, thereby providing appropriate information to the mobile device 10 or the user terminal 20. Alternatively, it may provide or process a function. For example, the processor 140 may estimate the posture of the mobile device 10 based on a sensing value measured by the sensor unit 130 . For example, the processor 140 may obtain movement inertia through an accelerometer, rotational inertia through a gyroscope, and information related to azimuth through a geomagnetic sensor. The processor 140 may obtain information (eg, IMU attitude information) on the speed or attitude of the inertial measurement device 100 by integrating the acceleration and angular velocity according to the movement. In an embodiment, the IMU attitude information (ie, attitude information of the inertial measurement device) may be information related to pitch, roll, and yaw.

In addition, the processor 140 may obtain GPS information from the GPS device 200, and determine whether or not to calibrate the IMU posture information (ie, whether or not to calibrate) based on the acquired GPS information.

A method for the processor 140 to perform calibration based on GPS information will be described in detail with reference to FIGS. 3 to 7 below.

3 shows an exemplary flowchart of a calibration method of an inertial measurement device related to an embodiment of the present invention. The order of the steps for performing the calibration method shown in FIG. 3 may be changed as needed, and at least one step may be omitted or added. That is, the above steps are only one embodiment of the present invention, and the scope of the present invention is not limited thereto.

According to an embodiment of the present invention, the processor 140 may obtain IMU attitude information and GPS information (S110). According to an embodiment, the processor 140 may obtain IMU attitude information. In this case, IMU posture information may be generated based on a value sensed by the sensor unit 130 . Specifically, the processor 140 may estimate the posture of the mobile device 10 based on the sensing value measured by the sensor unit 130 . For a specific example, the processor 140 may obtain information about the speed or attitude (eg, IMU attitude information) of the inertial measurement device 100 by integrating acceleration and angular velocity according to movement. In this case, since the inertial measuring device 100 is included in the moving device 10 , the acquired speed and attitude information may be information about the speed and attitude related to the moving device 10 . In an embodiment, the IMU attitude information (ie, attitude information of the inertial measurement device) may be information related to pitch, roll, and yaw. For example, the sensor unit 130 may include a 3-axis acceleration sensor, a 3-axis branch sensor, and a 3-axis gyro sensor (i.e., a gyroscope), each of which includes 3-axis angular velocities, that is, roll, pitch, and yaw angular velocities. The posture or movement of the mobile device 10 may be estimated by measuring . For example, the posture of the mobile device 10 can be described based on three axes, and as shown in FIG. 4, the forward and backward movement direction of the vehicle is set as the x-axis, and the left and right direction of the vehicle is set as the y-axis. and the vertical direction of the vehicle can be set as the z-axis.

In this case, the attitude information related to the pitch is attitude information related to the vehicle rotating on the y-axis, and may be, for example, information related to the attitude of the vehicle due to an angle difference in which the front part of the vehicle is above or below the rear part.

The attitude information related to the roll is attitude information related to the vehicle rotating around the x-axis, and may be, for example, information related to the attitude of the vehicle due to an angle difference in which the left part of the vehicle is higher or lower than the right part. .

The attitude information related to the yaw is attitude information related to the vehicle rotating on the z-axis, and may be, for example, information related to the attitude of the vehicle related to right turn or left turn.

In an embodiment, the IMU attitude information may include one or more IMU attitude sub-information related to pitch, roll and yaw, respectively. For example, the first IMU attitude sub-information may be related to pitch, the second IMU attitude sub-information may be related to roll, and the third IMU attitude sub-information may be information related to yaw. The detailed numerical description related to each IMU attitude sub-information described above is only an example, and the present invention is not limited thereto.

The GPS information may be information obtained through the GPS device 200. The GPS device 200 is a satellite system that transmits microwaves through a special frequency, and may refer to a system or device that generates location information from information acquired based on a signal received from a satellite. For example, the GPS device may obtain GPS information by calculating a distance by calculating a time required for a signal transmitted from a satellite to reach a receiving device based on a signal received from the satellite. Here, the GPS information may include information obtained by measuring positioning values of the mobile device 10 and expressing the corresponding positioning values in the form of latitude and longitude values. Additionally, the GPS information may further include information about altitude. In other words, GPS information may include information about latitude, longitude, and altitude related to a location.

In the embodiment, the GPS information may be characterized in that it is acquired through the GPS device 200 provided in at least one of the inertial measurement device 100 and the mobile device 10 equipped with the inertial measurement device 100. . Specifically, the inertial measurement device 100 of the present invention may be provided in one area of the mobile device 10 . In addition, the GPS device 200 may be provided in either the inertial measurement device 100 or the mobile device 10 . In other words, GPS information obtained through the GPS device 200 may be movement of the mobile device 10 or location information related to movement. For example, the GPS information may include location information related to the latitude, longitude, and altitude of the mobile device 10 .

In one embodiment, the GPS device 200 may be characterized in that it is provided with a plurality. Each of the plurality of GPS devices 200 may be provided at positions corresponding to each other within the mobile device 10 . The plurality of GPS devices 200 may be provided to estimate information about the posture of the mobile device 10 based on each GPS information obtained through each GPS device.

According to an embodiment of the present invention, the processor 140 may obtain GPS attitude information based on GPS information (S120). In this case, the GPS attitude information relates to attitude information estimated through GPS information, and may include one or more GPS attitude sub-information.

According to an embodiment, the processor 140 may obtain first GPS attitude sub-information based on the amount of change in GPS information. The processor 140 may obtain first GPS information based on the first point of view and obtain second GPS information based on a second point of view that is a point of time after the first point of time. In addition, the processor 140 may obtain first GPS attitude sub-information based on a comparison between the first GPS information and the second GPS information, that is, based on the amount of change in the GPS information according to the viewpoint. For example, when the first GPS information corresponding to the first time point includes information that corresponds to a higher altitude than the second GPS information corresponding to the second time point, the vehicle is moving downward, so the front of the vehicle is lowered. can be For another example, if the first GPS information corresponding to the first time point includes information that corresponds to a lower altitude than the second GPS information corresponding to the second time point, since the vehicle is moving upward, the front of the vehicle is raised. may be in a state That is, the processor 140 may obtain first GPS attitude sub-information in response to the amount of change in GPS information. In this case, the first GPS attitude sub-information may be attitude information about the pitch of the mobile device 10 . In other words, the processor 140 infers the driving direction of the mobile device according to the amount of change in GPS information (ie, altitude), and in response thereto, the first GPS attitude sub-information related to the front and rear of the mobile device 10, that is, the pitch. can be obtained.

Also, in an embodiment, the processor 140 may obtain GPS attitude information including one or more GPS attitude sub information based on at least one of a plurality of GPS attitude sub information.

In an embodiment, the GPS information may include a plurality of GPS sub information obtained from each of a plurality of GPS devices.

In detail, the processor 140 sets the second GPS position based on the first GPS sub-information and the second GPS sub-information obtained through the first and second GPS devices provided to correspond to each other on the left and right sides of the mobile device 10, respectively. Sub information can be obtained.

That is, among the plurality of GPS devices 200, the first GPS device may be provided on the left side of the mobile device 10 (eg, vehicle), and the second GPS device may be provided on the right side of the mobile device 10 (ie, the first GPS device). It may be provided at a position corresponding to the provided position). Here, information on the attitude of the mobile device 10 (ie, 2nd GPS attitude information) may be obtained through first GPS sub-information obtained from the first GPS device and second GPS sub-information obtained from the second GPS device. For a more specific example, when the first GPS sub information includes information indicating that the first GPS sub information is located at a higher altitude than the second GPS sub information, the processor 140 assumes that the left side of the current mobile device (ie, vehicle) is tilted higher than the right side. Information, that is, second GPS attitude sub-information may be generated. This 2nd GPS position information may be position information regarding the roll of the mobile device 10 . That is, the processor 140 may obtain the second GPS attitude sub-information related to the roll based on the first GPS sub-information and the second GPS sub-information obtained corresponding to the left and right sides of the mobile device 10, respectively.

In addition, the processor 140 provides a third GPS position sub-information based on the 3GPS sub-information and the 4th GPS sub-information obtained through the 3rd and 4th GPS devices provided to correspond to the front and back of the mobile device 10, respectively. information can be obtained.

Specifically, among the plurality of GPS devices 200, the 3rd GPS device may be provided in the front area of the mobile device 10 (eg, vehicle), and the 4th GPS device may be provided in the rear area of the mobile device 10 (ie, the 1st GPS device). It may be provided at a location corresponding to the location of the 3GPS device). Here, information on the attitude of the mobile device 10 (ie, 3GPS attitude information) may be obtained through 3GPS sub-information obtained from the 3GPS device and 4GPS sub-information obtained from the 4th GPS device. For a more specific example, based on the difference in latitude and longitude related to the front and rear through the 3GPS sub-information and the 4th GPS sub-information, the processor 140 determines how much the current mobile device (ie, vehicle) is bent with respect to the z-axis. It is possible to generate attitude information about, that is, 3GPS attitude sub-information. This 3GPS attitude information may be attitude information about the yaw of the mobile device 10 . That is, the processor 140 may obtain 3GPS posture sub-information related to yaw based on the 3GPS sub-information and the 4th GPS sub-information obtained corresponding to the front and rear of the mobile device 10, respectively.

In other words, the processor 140 includes GPS devices in each of the left, right, front, and rear areas of the mobile device 10 that correspond to each other, and based on comparison of GPS sub-information obtained from the GPS devices at the corresponding positions. Thus, GPS attitude information related to roll and yaw (ie, 2nd GPS attitude sub-information and 3rd GPS attitude sub-information) can be obtained.

In summary, the processor 140 obtains first GPS attitude sub-information, second GPS attitude sub-information, and third GPS attitude sub-information corresponding to pitch, roll, and yaw, respectively, through a plurality of GPS devices provided in the mobile device 10. can do. Through each of these GPS attitude sub-information, the processor 140 can estimate the attitude of the mobile device 10 in relation to the 3D.

According to an embodiment of the present invention, the processor 140 may determine whether to correct the IMU attitude information based on the comparison between the GPS attitude information and the IMU attitude information (S130). Specifically, the processor 140 may perform a comparison between the IMU attitude information obtained based on the sensed value of the sensor unit 130 and the GPS attitude information obtained from a plurality of GPS devices, and if the respective attitude information does not match If not, for example, when the degree of agreement of each attitude information is equal to or less than a preset threshold degree of agreement, it may be determined that the IMU attitude information needs correction. The processor 140 determines whether or not the IMU attitude information is corrected through whether the IMU attitude sub-information related to the pitch matches the 1st GPS attitude sub-information, or whether the IMU attitude sub-information related to the roll matches the 2nd GPS attitude sub-information. It is possible to determine whether the IMU attitude information is corrected or not, or whether the IMU attitude information is corrected through whether IMU attitude sub-information related to yaw and 3GPS attitude sub-information match.

That is, if there is a difference between the IMU attitude information and the GPS attitude information, the processor 140 determines that an error has occurred in the reference point of the inertial measurement device 100, and performs correction so that the IMU attitude information indicates an accurate attitude, that is, calibration can do.

According to an embodiment of the present invention, the processor 140 may perform correction of the IMU attitude information based on a result of determining whether the IMU attitude information is corrected (S140). Here, calibration may mean calibration of setting an accurate reference point of the inertial measurement device 100 . The processor 140 may perform calibration on the IMU attitude information acquired through the sensor unit 130 based on the GPS attitude information. For example, the processor 140 may perform calibration so that the IMU attitude information becomes the same as the GPS attitude information. For example, the processor 140 may correct the pitch-related attitude by correcting the pitch-related IMU attitude sub-information to be equal to the 1st GPS attitude sub-information. For example, when the pitch-related IMU attitude sub-information and the 1st GPS attitude sub-information indicate opposite directions (eg, east and west, respectively), the processor 140 may calibrate the IMU attitude sub-information. . In this case, by calibration of the processor 140, the inertial measurement device 100 may determine that the attitude information related to the pitch of the mobile device 10 is related to the west.

Further, for example, the processor 140 corrects each of the IMU attitude sub-information related to roll and the IMU attitude sub-information related to yaw in correspondence to the 2nd GPS attitude sub-information and the 3rd GPS attitude sub-information, respectively, so that the roll and yaw You can also correct your posture.

That is, the processor 140 may perform calibration to correct a difference between an internal sensor value (ie, IMU attitude information) and an actual value based on GPS information (ie, GPS attitude information).

In another embodiment of the present invention, the processor 140 may perform correction of IMU attitude information based on GPS attitude estimation information related to the peripheral mobile device 10 . A detailed description of a method for calibrating IMU attitude information based on GPS attitude prediction information will be described later in detail with reference to FIG. 5 .

5 is a flowchart of a method for calibrating an inertial measurement device related to another embodiment of the present invention.

According to an embodiment, the processor 140 may obtain one or more pieces of nearby GPS information related to a nearby mobile device having the same traveling direction based on the GPS information (S210). Specifically, the processor 140 obtains the surrounding GPS information related to the surrounding GPS devices having the same traveling direction as the corresponding GPS information within a certain radius based on the GPS information obtained from the GPS device provided in the mobile device 10. can For example, the surrounding GPS information may be information acquired from a GPS device installed in another mobile device 10 that traveled on a corresponding road before the mobile device 10 . In an embodiment, surrounding GPS information related to GPS information may be received through an external server. In this case, the external server is a server that obtains GPS information through the GPS devices 200 related to each of the plurality of mobile devices 10, such as a traffic information management server or a traffic situation information server (eg, a navigation server). can include

In addition, the processor 140 may obtain GPS attitude prediction information based on one or more surrounding GPS information (S220). In an embodiment, the processor 140 may obtain position information of a nearby GPS device based on a variation amount of one or more pieces of nearby GPS information. For example, the processor 140 may obtain first GPS information related to the first mobile device 10 that previously traveled on the road on which the mobile device 10 currently travels at a previous point in time from an external server. In this case, the processor 140 may obtain GPS orientation prediction information based on the first GPS information. That is, the processor 140 may acquire GPS information of other mobile devices that have traveled on a specific road on which the mobile device 10 is currently driving, based on the GPS information of mobile devices that have passed the corresponding road at a previous point in time. Thus, GPS attitude prediction information can be obtained. For example, GPS attitude information derived from GPS information of the first mobile device 10 may be GPS attitude prediction information corresponding to the mobile device 10 . For example, the GPS attitude prediction information is a pitch calculated in relation to a change in GPS information (eg, an altitude change) of the first mobile device (ie, another vehicle that has traveled at a previous point in time corresponding to the road on which the mobile device is traveling). It may include attitude information about the position or position information about the roll and yaw calculated through a plurality of GPS devices provided in the first mobile device. In other words, the GPS attitude prediction information may be GPS attitude information related to another mobile device that traveled on the same road as the mobile device 10 at a previous time.

Also, the processor 140 may determine whether or not to calibrate the IMU attitude information based on the GPS attitude prediction information. To this end, the processor 140 may perform a comparison between GPS attitude prediction information and IMU attitude information (S230).

In an embodiment, the processor 140 may not perform correction on the IMU attitude information when the GPS attitude prediction information and the IMU attitude information coincide (S240). That is, when the attitude information (GPS attitude prediction information) of the surrounding mobile devices and the IMU attitude information acquired through the internal sensor value are the same, the processor 140 determines that the reference point of the inertial measurement device 100 is well set, and the IMU Correction of attitude information may not be performed.

In an embodiment, when the GPS attitude prediction information and the IMU attitude information do not match, the processor 140 may perform correction of the IMU attitude information based on the GPS attitude prediction information (S250). If there is a difference between the IMU attitude information and the GPS attitude prediction information, the processor 140 determines that an error has occurred in the reference point of the inertial measurement device 100, and performs calibration so that the IMU attitude information indicates an accurate attitude, that is, calibration can do. The processor 140 may perform calibration on the IMU attitude information acquired through the sensor unit 130 based on the GPS attitude estimation information. For example, the processor 140 may perform calibration so that the IMU attitude information becomes the same as the GPS attitude prediction information. That is, the processor 140 corrects the difference between the internal sensor value (ie, IMU attitude information) and the actual value based on GPS information of another mobile device 10 in the same driving direction (ie, GPS attitude prediction information). can be done

In summary, the processor 140 identifies other mobile devices traveling in the same direction, and determines whether IMU attitude information (ie, attitude information related to actual sensed values) is corrected based on the GPS information of the identified mobile devices. can

In an additional embodiment, the processor 140 may calculate reliability of the GPS attitude information based on the GPS attitude prediction information, and determine whether or not to calibrate the IMU attitude information based on the reliability. A detailed description of a method for determining whether or not to calibrate the IMU attitude information based on the reliability of the GPS attitude information will be described later in detail with reference to FIG. 6 .

6 shows an exemplary flowchart of a method for performing calibration of an inertial measurement device based on reliability of GPS attitude information related to an embodiment of the present invention.

According to an embodiment, the processor 140 may compare GPS attitude prediction information and GPS attitude information to calculate reliability of the GPS attitude information (S310). Specifically, the processor 140 may calculate the reliability of the GPS attitude information based on the similarity between the GPS attitude prediction information and the GPS attitude information. For example, the processor 140 may calculate a higher reliability of the GPS attitude information as the GPS attitude prediction information and the GPS attitude information are similar, and lower the reliability of the GPS attitude information as the GPS attitude prediction information and the GPS attitude information are not similar. can be calculated That is, the processor 140 determines that the GPS attitude information (i.e., GPS attitude prediction information) of other mobile devices traveling in the same direction and the GPS attitude information of the mobile device 10 are similar, the GPS attitude acquired through the GPS device. It can be determined that the reliability of the information is high.

The processor 140 may determine whether the reliability of the GPS attitude information is greater than or equal to a critical reliability (S320). For example, the threshold reliability may mean a value that is a criterion for determining whether GPS attitude information obtained based on GPS information has reliability.

In an embodiment, when the reliability of the GPS attitude information is greater than or equal to the critical reliability level, the processor 140 may calibrate the IMU attitude information based on the corresponding GPS attitude information (S330). For example, when the reliability of the GPS attitude information is 90 and the threshold reliability is 85, the processor 140 may perform correction of the IMU attitude information based on the corresponding GPS attitude information. For example, the processor 140 may perform calibration so that the IMU attitude information becomes the same as the GPS attitude information. That is, the processor 140 may perform calibration to correct a difference between an internal sensor value (ie, IMU attitude information) and an actual value based on GPS information (ie, GPS attitude information).

In an embodiment, if the reliability of the GPS attitude information is less than the threshold reliability, the processor 140 may not perform calibration on the IMU attitude information (S340).

That is, when the GPS attitude information obtained through the GPS device and the IMU attitude information obtained through the internal sensor value are the same, the processor 140 determines that the reference point of the inertial measurement device 100 is well set, and correction may not be performed. In this case, attitude information (ie, IMU attitude information) estimated by the inertial measurement device 100 may be more accurate than attitude information (GPS attitude information) obtained through GPS.

In summary, by comparing GPS attitude information (i.e., GPS attitude prediction information) of neighboring mobile devices and GPS information to calculate reliability, the GPS attitude information acquired through GPS information is reliable enough to determine whether IMU attitude information is calibrated or not. It can be determined whether or not it has This can improve accuracy in estimating the attitude of the mobile device 10 by performing calibration through GPS attitude information whose reliability is equal to or higher than a predetermined reference value. Furthermore, it is possible to provide an effect of improving driving stability of the mobile device 10 by improving the accuracy of posture estimation.

In another embodiment of the present invention, the processor 140 may obtain the latest attitude information based on the history information of the mobile device, and perform correction of the IMU attitude information based on the acquired latest attitude information. A detailed description of a method of performing correction for IMU attitude information based on the latest attitude information will be described later in detail with reference to FIG. 7 .

7 shows an exemplary flowchart of a calibration method of an inertial measurement device related to another embodiment of the present invention.

According to the embodiment, the processor 140 may acquire the latest posture information (S410). The latest posture information may be information on a posture obtained last in correspondence to a predetermined previous time point. For example, attitude information obtained lastly 10 minutes ago, which is a predetermined previous time point, may be the latest attitude information. The specific numerical description related to the acquisition of the latest attitude information described above is only an example, and the present invention is not limited thereto. In an embodiment, the latest posture information may include information related to a last acquired posture when there is no change in posture information for a predetermined period of time. For example, when the posture change of the mobile device 10 does not occur for 20 minutes, posture information obtained last 20 minutes ago may be the latest posture information. That is, the latest attitude information relates to the last attitude information of the mobile device 10, and may include GPS attitude information and IMU attitude information.

The processor 140 may perform calibration of the IMU attitude information based on the latest attitude information. To this end, the processor 140 may perform a comparison between the latest attitude information and the IMU attitude information (S420).

In an embodiment, the processor 140 may not perform correction on the IMU attitude information when the latest attitude information and the IMU attitude information coincide (S430). That is, when the most recent attitude information (ie, the latest attitude information) of the mobile device 10 and the IMU attitude information obtained through the internal sensor value are the same, the processor 140 determines that the reference point of the inertial measurement device 100 is well established. It is determined that it is held, and calibration for the IMU attitude information may not be performed.

In an embodiment, when the latest attitude information and the IMU attitude information do not match, the processor 140 may perform correction on the IMU attitude information based on the latest attitude information (S440). When there is a difference between the IMU attitude information and the latest attitude information, the processor 140 determines that an error has occurred in the reference point of the inertial measurement device 100 and performs calibration so that the IMU attitude information indicates an accurate attitude, that is, calibration. can For example, when the mobile device 10 does not move for a certain period of time, it may be difficult for the inertial measurement device 100 to accurately estimate the posture of the mobile device 10 . Specifically, since there is no change in posture of the mobile device 10 and there are no sensing values obtained through an accelerometer and a gyroscope, it may be difficult to accurately estimate a posture based on a relative change. In this case, when the moving device 10 tries to move forward by an external control signal, the position of the moving device 10 can be accurately estimated, but an error in estimating the accurate attitude of the inertial measurement device 100 direction estimation (eg, direction relative to forward or backward) may not be clear. Errors in direction estimation may cause serious accidents.

The processor 140 of the present invention may obtain the latest attitude information related to the most recent attitude information, and perform correction on the IMU attitude information when the acquired latest attitude information is different from the IMU attitude information. That is, the processor 140 may perform calibration on the IMU attitude information acquired through the sensor unit 130 based on the latest attitude information. For example, the processor 140 may perform calibration so that the IMU posture information becomes the same as the latest posture information.

In summary, since the processor 140 may perform correction on the IMU attitude information based on the latest attitude information related to the last acquired attitude information, when there is no motion in the mobile device 10 for a certain period of time (ie, Even when there is no change in posture), information on accurate posture estimation can be provided. This can provide an effect of improving driving stability of the mobile device by minimizing an error in direction estimation that may occur instantaneously.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as “software”) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory. device (eg, EEPROM, card, stick, key drive, etc.), but is not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information. The term “machine-readable medium” includes, but is not limited to, wireless channels and various other media that can store, hold, and/or convey instruction(s) and/or data.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged while remaining within the scope of the present invention. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present invention. Thus, the present invention is not to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

10: mobile device
20: user terminal
100: inertial measurement device
200: GPS device

Claims (12)

A method performed on one or more processors of a computing device, comprising:
Acquiring IMU attitude information and GPS information;
obtaining GPS orientation information based on the GPS information;
determining whether the IMU attitude information is corrected based on a comparison between the GPS attitude information and the IMU attitude information; and
performing correction on the IMU attitude information based on the determination result;
including,
Calibration methods for inertial measurement devices.
According to claim 1,
The GPS information,
It is characterized in that it is obtained through a GPS device provided in at least one of an inertial measurement device and a mobile device equipped with the inertial measurement device,
The IMU attitude information,
Including one or more IMU attitude sub-information related to pitch, roll and yaw, respectively,
Calibration methods for inertial measurement devices.
According to claim 2,
The GPS device,
It is characterized in that it is provided in plurality,
Each of the plurality of GPS devices,
Characterized in that they are provided at positions corresponding to each other in the mobile device,
Calibration methods for inertial measurement devices.
According to claim 3,
The GPS information,
Includes a plurality of GPS sub information obtained from each of the plurality of GPS devices,
Obtaining GPS attitude information based on the GPS information,
obtaining the GPS attitude information including one or more GPS attitude sub information based on at least one of the plurality of GPS attitude sub information;
including,
Calibration methods for inertial measurement devices.
According to claim 4,
Obtaining the GPS attitude information,
obtaining first GPS attitude sub-information based on the amount of change in the GPS information;
including,
Calibration methods for inertial measurement devices.
According to claim 4,
Obtaining the GPS attitude information,
obtaining second GPS attitude sub-information based on first and second GPS sub-information obtained through first and second GPS devices provided to correspond to the left and right sides of the mobile device, respectively; and
obtaining 3GPS attitude sub-information based on 3GPS sub-information and 4GPS sub-information obtained through a 3GPS device and a 4GPS device provided to correspond to the front and back of the mobile device, respectively;
including,
Calibration methods for inertial measurement devices.
According to claim 2,
The step of determining whether the correction is made,
determining whether the IMU attitude information is corrected through whether the IMU attitude sub-information related to the pitch and the 1st GPS attitude sub-information match;
determining whether or not the IMU attitude information is corrected through whether the IMU attitude sub-information related to the roll coincides with the 2nd GPS attitude sub-information; and
determining whether IMU attitude information is corrected through whether IMU attitude sub-information related to yaw and 3GPS attitude sub-information match;
Including at least one step of
Calibration methods for inertial measurement devices.
According to claim 1,
The method,
obtaining one or more pieces of nearby GPS information related to a nearby mobile device having the same traveling direction based on the GPS information;
obtaining GPS orientation prediction information based on the one or more surrounding GPS information; and
determining whether or not to calibrate the IMU attitude information based on a comparison between the GPS attitude prediction information and the IMU attitude information;
Including more,
Calibration methods for inertial measurement devices.
According to claim 8,
The step of determining whether to calibrate the IMU attitude information,
calculating reliability of the GPS attitude information based on a comparison between the GPS attitude prediction information and the GPS attitude information; and
determining whether or not to calibrate the IMU attitude information based on whether the reliability of the GPS attitude information is greater than or equal to a threshold reliability;
including,
Calibration methods for inertial measurement devices.
According to claim 1,
The method,
acquiring latest posture information; and
determining whether to correct the IMU attitude information based on a comparison between the latest attitude information and the IMU attitude information;
Including more,
Calibration methods for inertial measurement devices.
a memory that stores one or more instructions; and
a processor to execute the one or more instructions stored in the memory;
By executing the one or more instructions, the processor:
An apparatus that performs the method of claim 1 .
A computer program stored in a computer-readable recording medium to be combined with a computer, which is hardware, to perform the method of claim 1.

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