Movement amount estimation device, movement amount estimation method, and storage medium

Abstract

The positioner ECU (100) as a movement amount estimation device of the present application estimates a movement amount of a vehicle (A) equipped with a GNSS receiver (10) that receives satellite data from positioning satellites from a past position to a current position. The positioner ECU has a priority decision section (130) that decides a level of priority used in estimation of the movement amount for each combination of past satellite data acquired by the receiver at a plurality of past positions and current satellite data acquired at the current position. The positioner ECU has a movement amount calculation section (140) that calculates a relative movement amount from the past position to the current position based on combinations of a high priority range in which the priority is higher than a low priority range among the plurality of combinations.

Description

[0001] Cross-references to related applications

[0002] This application is based on Japanese Patent Application No. 2021-76513, filed in Japan on April 28, 2021, and incorporates the contents of the base application in its entirety by reference. Technical Field

[0003] The disclosure in this specification relates to techniques for estimating the amount of movement of a moving body over time. Background Technology

[0004] Non-patent document 1 discloses a technique for estimating the movement of a mobile body equipped with a receiver by performing time difference RTK (Real Time Kinematic) positioning based on past and current data from the receiver.

[0005] Non-patent literature 1: T. Suzuki (2020), “Time-Relative RTK-GNSS GNSS Loop Closure in Pose Graph Optimization”, IEEE ROBOTICS AND AUTOMATION LETTERS, VOL.5, NO.3, JULY 2020

[0006] In time-difference RTK localization, there may be multiple past data combinations that can be combined with the current data. However, performing time-difference RTK on all selectable combinations can increase the computational load. Summary of the Invention

[0007] The purpose of this disclosure is to provide a motion estimation device, a motion estimation method, and a motion estimation program that can suppress computational load.

[0008] The various methods disclosed in this specification employ different technical means to achieve their respective purposes. Furthermore, the reference numerals within parentheses in the claims are merely examples indicating the correspondence with specific units described in a subsequent embodiment as a method, and are not intended to limit the scope of the technology.

[0009] One disclosed mobility estimation device is a mobility estimation device that estimates the amount of movement of a mobile body equipped with a receiver from a past location to its current location. The receiver receives satellite data from positioning satellites. This mobility estimation device includes:

[0010] The prioritization unit determines the priority level to be used in motion estimation based on a combination of past satellite data acquired by the receiver at multiple past locations and current satellite data acquired at the current location; and

[0011] The movement calculation unit calculates the relative movement from the past location to the current location based on combinations of high-priority ranges that have a higher priority than low-priority ranges.

[0012] One publicly disclosed method for estimating mobility is a method executed by a processor to estimate the amount of movement of a mobile body equipped with a receiver from a past location to its current location. The receiver receives satellite data from positioning satellites. This mobility estimation method includes:

[0013] Prioritization determines the process, ranking the data used in motion estimation by combining past satellite data acquired by receivers at multiple past locations with current satellite data acquired at the current location; and

[0014] The movement calculation process calculates the relative movement from the past location to the current location based on combinations of high-priority ranges that have a higher priority than low-priority ranges.

[0015] One publicly available mobility estimation program is a multi-command program executed by a processor to estimate the mobility of a mobile object equipped with a receiver that receives satellite data from positioning satellites from a past location to its current location.

[0016] The command includes:

[0017] Prioritization determines the process, ranking the data used in motion estimation by combining past satellite data acquired by receivers at multiple past locations with current satellite data acquired at the current location; and

[0018] The movement calculation process calculates the relative movement from the past location to the current location based on combinations of high-priority ranges that have a higher priority than low-priority ranges.

[0019] According to these disclosures, the priority used in motion estimation is determined by combining past satellite data acquired by receivers at multiple past locations with current satellite data acquired at the current location. Furthermore, the relative motion from the past location to the current location is calculated based on combinations of high-priority ranges where the priority is higher than the low-priority ranges. Therefore, it is possible to appropriately determine the data combining multiple past satellite data with current satellite data based on priority. This further reduces the use of large amounts of past satellite data. Therefore, a motion estimation apparatus, a motion estimation method, and a motion estimation program that reduce computational load can be provided. Attached Figure Description

[0020] Figure 1 It is a block diagram showing the overall structure of a system including a positioner ECU.

[0021] Figure 2 This is a flowchart illustrating an example of a movement estimation method performed by the positioner ECU.

[0022] Figure 3 It means Figure 2 The detailed processing flowchart is as follows.

[0023] Figure 4 It means Figure 2 The detailed processing flowchart is as follows.

[0024] Figure 5 It means Figure 2 The detailed processing flowchart is as follows.

[0025] Figure 6 This is a block diagram showing the overall structure of the system including the positioner ECU in the second embodiment.

[0026] Figure 7 This is a flowchart illustrating an example of a movement estimation method performed by the positioner ECU in the second embodiment.

[0027] Figure 8 It means Figure 7 The detailed processing flowchart is as follows.

[0028] Figure 9 This indicates the third embodiment. Figure 7 The detailed processing flowchart is as follows.

[0029] Figure 10 This is a block diagram showing the overall structure of the system including the positioner ECU in the fourth embodiment.

[0030] Figure 11 This is a flowchart illustrating an example of a movement estimation method performed by the positioner ECU in the fourth embodiment. Detailed Implementation

[0031] (First Implementation)

[0032] like Figure 1 As shown, a movement estimation device according to one embodiment of the present disclosure is provided by a positioner ECU 100. The positioner ECU 100 is an electronic control unit mounted on a vehicle A, which is a moving body.

[0033] The locator ECU 100 can acquire various information, for example, via an in-vehicle network including at least one of a LAN (Local Area Network), wiring harness, and internal bus. The information acquired from the in-vehicle network includes, for example, positioning signals from the GNSS receiver 10.

[0034] GNSS receiver 10 receives navigation signals transmitted from positioning satellites that constitute the GNSS (Global Navigation Satellite System). GNSS receiver 10 generates observation data based on the navigation signals and their carrier waves. As an example, the observation data includes pseudorange, carrier phase, Doppler frequency, carrier-to-noise ratio, and the presence or absence of cycle slips. GNSS receiver 10 sequentially provides the observation data to the positioner ECU 100.

[0035] The positioner ECU 100 is a computer comprising at least one memory 101 and a processor 102. The memory 101 is a non-transferable physical storage medium, such as semiconductor memory, magnetic media, and optical media, that stores or non-transfers programs and data that can be read by a computer. The memory 101 stores various programs executed by the processor 102, such as the movement estimation program described later.

[0036] The processor 102 includes, for example, at least one of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a RISC (Reduced Instruction Set Computer) CPU as its core. The processor 102 executes multiple commands contained in the motion estimation program stored in the memory 101. Thus, the positioner ECU 100 constructs multiple functional units for estimating the current position of vehicle A. In this way, in the positioner ECU 100, the motion estimation program stored in the memory 101 constructs multiple functional units by causing the processor 102 to execute multiple commands. Specifically, in the positioner ECU 100, such as... Figure 2 As shown, the system includes functional units such as a data acquisition unit 110, a simple positioning unit 120, a priority determination unit 130, a movement calculation unit 140, and a position estimation unit 150.

[0037] The data acquisition unit 110 acquires new observation data received by the GNSS receiver 10. The data acquisition unit 110 stores the new observation data in a buffer. In addition, the data acquisition unit 110 provides the data to the simplified positioning unit 120 and the priority determination unit 130 in sequence.

[0038] The simplified positioning unit 120 estimates the vehicle A's own position using a time-difference tracking (RTK) method described later. For example, the simplified positioning unit 120 can estimate its own position based on standalone positioning or satellite positioning such as DGPS. Hereinafter, the position estimated by the simplified positioning unit 120 will sometimes be referred to as a provisional position. Furthermore, the simplified positioning unit 120 calculates the distance traveled after the GNSS receiver 10 is powered on based on the estimated position. Additionally, the simplified positioning unit 120 can also calculate the distance traveled after the locator ECU 100 itself is powered on. Alternatively, the simplified positioning unit 120 can also calculate the distance traveled after the vehicle A starts. The simplified positioning unit 120 can calculate the travel distance using a dead reckoning method based on vehicle speed information detected by a vehicle speed sensor, etc. Furthermore, the simplified positioning unit 120 can also calculate the estimated speed of the vehicle A from the Doppler velocity of the GNSS receiver 10 calculated based on the Doppler frequency.

[0039] The buffer update unit 125 updates the buffer based on the observation data and the positioning result data in the simplified positioning unit 120. Specifically, if buffer data was acquired before or earlier than the threshold time, the buffer update unit 125 deletes that data. Furthermore, the buffer update unit 125 stores the latest acquired observation data and positioning result data as new buffer data in the memory 101. The positioning result data includes the provisional self-position and the distance traveled after the GNSS receiver 10 was powered on. Alternatively, instead of the provisional self-position, the current position estimated by the position estimation unit 150 (described later) can be stored.

[0040] Priority determination unit 130 determines the priority level used for estimating mobility based on each combination of past satellite data acquired at multiple past locations and current satellite data acquired at the current location.

[0041] The priority determination unit 130 can compare priorities by quantifying them based on multiple indicators. These indicators include, for example, three parameters: range difference indicator, time difference indicator, and satellite quality indicator.

[0042] The distance difference index is an index representing the difference between the distance traveled from a reference point to a past satellite data acquisition location (past location) and the distance traveled from the reference point to the current satellite data acquisition location (current location). For example, the reference point is set as the location where the GNSS receiver 10 is powered on. The distance difference index is, for example, a normalized index of the distance difference between the distances traveled from when the GNSS receiver 10 was powered on. Specifically, the priority determination unit 130 calculates the distance difference index based on the following formula 1. Hereinafter, id1 is the distance difference index. D is the distance difference between the distance traveled from when the GNSS receiver 10 was powered on at the current satellite data acquisition location and the distance traveled from when the GNSS receiver 10 was powered on at the past satellite data acquisition time. Nmin is the normalized minimum value, and Nmax is the normalized maximum value. The normalized minimum value and the normalized maximum value are predetermined values.

[0043] (number 1)

[0044] id1=(D-Nmin) / (Nmax-Nmin)…(1)

[0045] Furthermore, when the distance difference is less than the normalized minimum value, the priority determination unit 130 is set to D = Nmin. Conversely, when the distance difference is greater than the normalized maximum value, the priority determination unit 130 is set to D = Nmax.

[0046] The time difference index is an indicator representing the elapsed time from the time of acquiring past satellite data to the time of acquiring current satellite data. Based on Equation 2 below, the time difference index is normalized in the same way as the range difference index. Hereinafter, id2 is the time difference index. Furthermore, T is the difference between the elapsed time from the time the GNSS receiver 10 was powered on at the time of acquiring current satellite data and the elapsed time from the time the GNSS receiver 10 was powered on at the time of acquiring past satellite data.

[0047] (Number 2)

[0048] id2=(Nmin-T) / (Nmax-Nmin)…(2)

[0049] Furthermore, similar to the case of distance difference, when the time difference is less than the normalized minimum value, the priority determination unit 130 is set to T = Nmin, and when the time difference is greater than the normalized maximum value, it is set to T = Nmax.

[0050] Satellite quality metrics are indicators of the quality of acquired satellite data. For example, a satellite quality metric is a normalized number of signals with cycle slips from the time of acquisition of past satellite data to the time of acquisition of the current satellite data. Based on the following formula (3), the satellite quality metric is normalized in the same way as the range difference metric. Hereinafter, id3 is the satellite quality metric, and N is the number of signals.

[0051] (Number 3)

[0052] id3=(N-Nmin) / (Nmax-Nmin)…(3)

[0053] Furthermore, similar to the cases of distance difference and time difference, when the number of signals is less than the normalized minimum value, the priority determination unit 130 sets N = Nmin, and when the time difference is greater than the normalized maximum value, it sets N = Nmax. Additionally, the values ​​of Nmax and Nmin are different in the above three formulas.

[0054] The priority determination unit 130 calculates the sum of the above indicators as the priority. The priority determination unit 130 calculates the priority for each buffered past satellite data and provides the results to the mobility calculation unit 140 in sequence.

[0055] The priority determination unit 130 can assign weights to parameters. For example, the priority determination unit 130 assigns a weight based on vehicle speed to the distance difference. Specifically, the priority determination unit 130 adjusts the weights so that when the vehicle speed falls within a specified range, the weight of the distance difference becomes smaller (e.g., becomes zero) compared to when the speed falls outside the specified range. Here, the specified range refers to a numerical range where the vehicle speed is below or less than a threshold. Furthermore, the priority determination unit 130 can also assign weights to other priority parameters corresponding to specified conditions.

[0056] The mobility calculation unit 140 calculates the relative mobility from the past location to the current location based on combinations of high-priority ranges with priorities higher than low-priority ranges. For example, the mobility calculation unit 140 selects a predetermined amount (e.g., ten) starting from the highest-priority combination as the combinations used for mobility calculation. In this case, the priority range result including each priority in each selected combination becomes the high-priority range, and the priority range including each priority of unselected combinations is included in the low-priority range. Furthermore, the mobility calculation unit 140 may also set a threshold that serves as the boundary between the high-priority range and the low-priority range, and select combinations that are above or exceed this threshold.

[0057] The motion calculation unit 140 performs time difference RTK based on selected past satellite data. Therefore, the motion calculation unit 140 estimates the motion of vehicle A from the time each past satellite data was acquired to the time the current satellite data was acquired. The motion calculation unit 140 calculates the motion vector from the past position to the current position as the motion amount. The motion calculation unit 140 provides the calculated motion vector to the position estimation unit 150.

[0058] The time-difference RTK implemented by the movement calculation unit 140 will be explained. Furthermore, in this embodiment, "implementing time-difference RTK" at least includes the calculation of the relative movement amount (movement vector) up to the moving body (vehicle A in this embodiment), and does not necessarily include an estimate of the absolute position of the moving body based on that movement amount. Additionally, in the following description, RTK positioning that calculates the baseline vector from a known reference station to the moving body is sometimes referred to as conventional RTK to distinguish it from time-difference RTK.

[0059] Time difference RTK is a positioning method that calculates the relative movement vector of a moving object from a past moment to the present moment by replacing the reference station in a typical RTK with the moving object at a past moment.

[0060] In detail, in time-difference-time (RTK) positioning, the first difference in carrier phase is the difference between the carrier phase of the positioning signal received by GNSS receiver 10 at the current moment and the carrier phase of the positioning signal received by the same GNSS receiver 10 at a past moment. Therefore, the difference between the first difference in a specific reference satellite and the first difference in other satellites constitutes the double difference in time-difference-time (RTK).

[0061] The motion calculation unit 140 calculates the double difference based on buffered past satellite data and current satellite data. The motion calculation unit 140 calculates the double difference between the reference satellite and multiple other satellites (e.g., four or more). Based on the calculated double differences, the motion calculation unit 140 performs parameter estimation using methods such as least squares or Kalman filtering. Thus, the motion calculation unit 140 calculates the relative motion vector of the moving body from a past time to the present time.

[0062] The position estimation unit 150 calculates the current position of vehicle A as an estimated position based on the movement vector. For example, the position estimation unit 150 combines the position estimated based on the past position and the movement vector with the position estimated in the simplified positioning unit 120 without using time difference (RTK) to estimate the current position. The position estimation unit 150 can use the average of the two position information as the current position, or it can estimate the current position by using Kalman filtering with the two position information as observations. The position estimation unit 150 outputs the estimated current position as the vehicle A's own position to the vehicle-mounted device 90. Here, the vehicle-mounted device 90 is a device that executes an application that uses the current position estimated by the locator ECU 100. In addition, the position estimation unit 150 can also replace the vehicle-mounted device 90 to send the current position to a server device or other external structures of vehicle A.

[0063] Next, the following is in order Figure 2 The flow of the motion estimation method executed by the positioner ECU 100 through the cooperation of functional modules will be described. Furthermore, in the flow described later, "S" refers to multiple steps of the flow executed by multiple commands contained in the program.

[0064] First, in S100, the data acquisition unit 110 acquires the latest satellite data received by the GNSS receiver 10. Next, in S110, the data acquisition unit 110 updates the buffer using the acquired satellite data. Specifically, the data acquisition unit 110 deletes satellite data from the buffer that has been acquired for a period of time or longer than the threshold time. Then, the data acquisition unit 110 appends the latest satellite data to the buffer.

[0065] Following the processing in S110, in S120, the priority determination unit 130 determines whether past satellite data (past data) exists. If it is determined that past data exists, the process moves to S130.

[0066] In S130, the priority determination unit 130 calculates the travel distance difference as one of the parameters used to determine the priority.

[0067] Reference Figure 3 The flowchart below provides a detailed explanation of the processing in S130. First, in S131, the current travel distance is obtained. Next, in S132, the travel distances at the time of acquisition of past satellite data for the combined object are obtained. Then, in S133, the difference between the two travel distances is calculated. Next, in S134, the difference is normalized. This normalized difference is the travel distance difference.

[0068] Return to Figure 2 In S140, the priority determination unit 130 calculates the time difference as one of the parameters used to determine the priority. (See reference...) Figure 4 The flowchart below details the processing in S140. First, in S141, the current time is obtained. Next, in S142, the times when the past satellite data was acquired for the combined object are obtained. Furthermore, the time here can be the elapsed time since the power-on of the GNSS receiver 10 was used as the reference time. Then, in S143, the difference between the two times is calculated. Next, in S144, the difference is normalized. This normalized difference is the time difference.

[0069] Return to Figure 2 In S150, the priority determination unit 130 calculates the satellite mass as one of the parameters used to determine priority. (See reference...) Figure 5 The flowchart below provides a detailed explanation of the processing in S150. First, in S151, signals without cycle slips are extracted from the current time to the target time (past time). Then, in S152, the number of signals without cycle slips is calculated and normalized. This normalized number of signals is set as the satellite quality index.

[0070] Return to Figure 2 In S170, the priority determination unit 130 selects the highest priority satellite data from the buffered satellite data. Here, the priority determination unit 130 uses the sum of the three parameters mentioned above as the priority. Next, in S180, the priority determination unit 130 determines whether the number of selected satellite data has reached the upper limit (e.g., about ten). If it is determined that the upper limit has not been reached, the process proceeds to S190. On the other hand, if it is determined that the upper limit has been reached, the process proceeds to S210.

[0071] In S190, the priority determination unit 130 removes satellite data stored in the buffer whose movement distance difference from the selected satellite data is below a threshold. In the following S200, the priority determination unit 130 determines whether any selectable satellite data remains. If it determines that satellite data remains, the process returns to S170. Conversely, if it determines that no satellite data remains, the process proceeds to S210. Through this series of processes from S170 to S200, multiple satellite data sets with higher priority and relatively far apart positions of vehicle A at the time of acquisition are selected.

[0072] In S210, the movement calculation unit 140 performs time difference RTK based on a combination of selected past satellite data and current satellite data, and calculates the movement from each past position to the current position. Next, in S220, the position estimation unit 150 estimates the current position of vehicle A based on the movement. Specifically, then, in S230, the position estimation unit 150 sends the estimated current position to the vehicle-mounted device 90. Furthermore, the position estimation unit 150 can also send the current position to the outside of vehicle A. S130, S140, S150, S170, S180, S190, and S200 are examples of a "priority determination process," and S210 is an example of a "movement calculation process."

[0073] According to the first embodiment described above, the priority used for estimating mobility is determined based on the combination of past satellite data acquired by the GNSS receiver 10 at multiple past locations and current satellite data acquired at the current location. Furthermore, the relative mobility from the past location to the current location is calculated based on combinations of high-priority data with higher priorities than low-priority combinations. Therefore, it is possible to appropriately determine the data combining multiple past satellite data with current satellite data based on priority. Therefore, it is possible to further suppress the use of large amounts of past satellite data. Therefore, the computational load can be reduced in mobility estimation.

[0074] Furthermore, according to the first embodiment, the higher the priority of a combination where the difference between the distance traveled by vehicle A from the reference point to the past point and the distance traveled from the reference point to the current point is, the more effectively the calculation of the amount of movement can be utilized, based on the time difference (RTK) whose accuracy is not easily reduced even when the distance traveled increases.

[0075] Furthermore, according to the first embodiment, combinations with smaller elapsed time (i.e., smaller time differences) from past satellite data acquisition times to current satellite data acquisition times are assigned higher priority. In RTK positioning, larger time differences have a greater impact from variations in satellite clock errors, ionospheric delays, and tropospheric delays, potentially leading to decreased accuracy. Therefore, by prioritizing smaller time differences, combinations where larger time differences easily result in greater accuracy reductions are less likely to be used in motion calculations. This further improves the accuracy of motion calculations.

[0076] Furthermore, according to the first embodiment, combinations of higher-quality past satellite data are given higher priority. Therefore, combinations that more easily improve the accuracy of motion calculations can be utilized in motion measurement calculations. Thus, the accuracy of motion measurement calculations can be further improved.

[0077] Furthermore, according to the first embodiment, priority is determined based on multiple indicators, and weights are assigned to each indicator according to conditions. Therefore, it is easy to determine the priority corresponding to the driving state of vehicle A, and a more suitable combination can be used in the calculation of movement amount.

[0078] Furthermore, according to the first embodiment, the lower the vehicle A's speed, the smaller the weight of the distance difference index is set. At lower speeds, the possibility that the vehicle speed sensor may fail to detect the speed correctly due to slippage increases. This may occur, for example, in the case of a vehicle speed sensor that detects wheel speed. In such cases, by reducing the weight of the distance difference index, the impact of incorrect speed on the calculation of travel distance can be reduced.

[0079] (Second Implementation)

[0080] In the second embodiment, a variation of the positioner ECU100 in the first embodiment will be described. Figures 6-8 The elements that have been appended with the same reference numerals as those in the first embodiment are the same elements and have the same effect.

[0081] In the second embodiment, the locator ECU 100 obtains information from the map DB 20, external sensor 30 and internal sensor 40 via the vehicle network, in addition to the GNSS receiver 10.

[0082] Map DB20 is a non-volatile memory that stores map data such as link data, node data, road shapes, and structures. The map data can also be a 3D map composed of point groups representing feature points of road shapes and structures. Furthermore, the 3D map can also be generated from captured images using REM (registered trademark). Additionally, the map data can include traffic restriction information, road construction information, weather information, and traffic light information. The map data stored in Map DB20 can be updated periodically or continuously based on the latest information distributed from a server located outside vehicle A.

[0083] External sensor 30 is an autonomous sensor that monitors the external environment of vehicle A. External sensor 30 can be, for example, a LIDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging) that detects feature points of objects on the ground, or a peripheral surveillance camera that captures images of a defined area in front of vehicle A.

[0084] The internal sensor 40 is a sensor that detects physical quantities related to the inner boundary of vehicle A. For example, the internal sensor 40 includes a vehicle speed sensor that detects the travel speed of vehicle A and an IMU that detects the inertial force acting on vehicle A. The IMU is a structure capable of detecting angular velocity and acceleration. The IMU may be, for example, a six-axis motion sensor with a three-axis gyroscope sensor and a three-axis accelerometer sensor.

[0085] In the second embodiment, the data acquisition unit 110 acquires, in addition to observation data, information required for executing self-position estimation methods other than time-difference kinematics (RTK). For example, the data acquisition unit 110 acquires LiDAR point cluster information and point cluster map data stored in map DB20 as information required for scan matching. Alternatively, the data acquisition unit 110 may also acquire images captured by the camera and map data.

[0086] The simplified positioning unit 120 estimates its provisional position based on data acquired by the data acquisition unit 110. The simplified positioning unit 120 can estimate its provisional position based on scanning and matching point cluster information with point cluster map data, matching captured images with landmarks on the map, etc.

[0087] The buffer update unit 125 also stores the reliability of the estimated position, which will be described later, as buffer data. Alternatively, the buffer update unit 125 may also store the reliability of its provisional position.

[0088] The priority determination unit 130 also calculates a reliability index as an indicator for determining the priority. The reliability index is a parameter calculated based on the reliability of the estimated location of the past location. That is, in the second embodiment, the priority determination unit 130 calculates the priority based on four parameters.

[0089] The position estimation unit 150 calculates the position of vehicle A based on the provisional self-position estimated by the simplified positioning unit 120 and the movement calculated by the movement calculation unit 140. At this time, the position estimation unit 150 also calculates the reliability of the estimated self-position (position reliability). The position estimation unit 150 can calculate the normalized estimation error as a reliability index.

[0090] Next, the following is in order Figure 7 The flow of the motion estimation method executed by the positioner ECU 100 of the second embodiment through the cooperation of functional modules is described. (Additionally, related to...) Figure 2 The steps described in the accompanying drawings are the same as those in the first embodiment.

[0091] If the satellite quality indicators are calculated in S150, this process moves to S160. In S160, the priority determination unit 130 calculates the position reliability.

[0092] Reference Figure 8 The flowchart below details the processing in S160. First, in S161, the estimation error of the past position is obtained. Next, in S162, this estimation error is floated. Specifically, the squares and square roots of the three dimensions are calculated based on the estimation errors in the three directions. Then, in S163, the floated estimation error is normalized. This normalized estimation error is set as the position reliability. If processing in S163 is executed, the process proceeds to... Figure 7 S170. In addition, besides the steps shown in the first embodiment, S160 is included in an example of the "priority determination process".

[0093] According to the second embodiment described above, combinations with higher reliability of estimated locations from past locations are given higher priority. Therefore, combinations whose estimation accuracy of the current location based on past locations and movement can be preferentially selected.

[0094] (Third Implementation)

[0095] In the third embodiment, a variation of the positioner ECU100 in the first embodiment will be described. Figure 9 The elements that have been appended with the same reference numerals as those in the first embodiment are the same elements and have the same effect.

[0096] In the locator ECU 100 of the third embodiment, the position estimation unit 150 estimates the estimated position by scanning and matching based on point group information, similar to the second embodiment. In this case, the priority determination unit 130 can calculate the position reliability as one of the priority parameters, similar to the second embodiment. In this case, the priority determination unit 130 calculates the reliability index based on the entropy of the normal configuration of the point group used in the estimated position calculation.

[0097] In detail, the priority determination unit 130 calculates the elevation and azimuth components for the normal vector of each point in the point group. Then, the priority determination unit 130 discretizes each point into a two-dimensional grid of elevation and azimuth components. The priority determination unit 130 calculates the generation number of each grid by discretizing all points, and divides the generation number by the number of points in the point group to calculate the generation probability of each grid. If the number of grids is set to L and the generation probability of each grid is set to pi, the entropy H can be calculated according to the following formula (4). The priority determination unit 130 uses the value obtained by normalizing the entropy as a reliability index.

[0098] [Number 4]

[0099]

[0100] Next, the following is in order Figure 9 The flow of the motion estimation method executed by the positioner ECU 100 in the third embodiment through the cooperation of functional modules will be described. In the third embodiment, according to Figure 9 Process execution Figure 7 The detailed processing of S160 is as follows: First, in S165, the point group detected by the LiDAR device is acquired. Next, in S166, the normal vector of the point group is calculated. Then, in S167, the normal vector is discretized with elevation and azimuth angles, and the generation probability is calculated. Next, in S168, the entropy is calculated based on the generation probability and normalized. This normalized entropy becomes the reliability index. If processing in S168 is executed, this process proceeds to... Figure 7 S170.

[0101] (Fourth Implementation)

[0102] In the fourth embodiment, a variation of the positioner ECU100 in the first embodiment will be described. Figure 10 , 11 The elements that have been appended with the same reference numerals as those in the first embodiment are the same elements and have the same effect.

[0103] In the fourth embodiment, the locator ECU 100, in addition to the GNSS receiver 10, also obtains information from the vehicle-to-everything (V2N) communicator 50 via the vehicle network. The V2N communicator 50 is a communication module mounted on the vehicle A. The V2N communicator 50 has at least the capability for V2N (Vehicle to Cellular Network) communication based on communication standards such as LTE (Long Term Evolution) and 5G. The V2N communicator 50 can receive correction information used in typical RTK from reference stations around the vehicle A. The V2N communicator 50 sequentially provides the acquired correction information to the locator ECU 100.

[0104] In the fourth embodiment, the position estimation unit 150 estimates its own position using normal RTK when normal RTK can be performed. On the other hand, when normal RTK cannot be performed, the position estimation unit 150 estimates its own position based on time difference RTK. For example, if communication with the reference station is cut off, if the communication delay is large, if the reference station malfunctions, or if there is no reference station nearby, the position estimation unit 150 can determine that normal RTK cannot be performed.

[0105] Next, the following is in order Figure 11 The flow of the motion estimation method executed by the positioner ECU 100 of the fourth embodiment through the cooperation of functional modules is described. (Additionally, related to...) Figure 2 , 7 The steps described in the accompanying drawings are the same as those in the first embodiment.

[0106] After processing in S110, the process moves to S115. In S115, the position estimation unit 150 determines whether normal RTK can be performed. If it is determined that it can be performed, the process moves to S120. On the other hand, if it is determined that normal RTK cannot be performed, the process moves to S116.

[0107] In S116, the position estimation unit 150 estimates the distance from the reference station to vehicle A based on normal RTK. If the processing of S116 is performed, the process proceeds to S220. Furthermore, when moving from S116 to S220, the position estimation unit 150 performs an estimation of the current position based on the distance from the reference station to vehicle A in S220.

[0108] (Other implementation methods)

[0109] The disclosure in this specification is not limited to the illustrated embodiments. The disclosure includes the illustrated embodiments and modifications made by those skilled in the art based on these embodiments. For example, the disclosure is not limited to the combination of components and / or elements shown in the embodiments. The disclosure can be implemented in various combinations. The disclosure can have additional portions that can be added to the embodiments. The disclosure includes embodiments with components and / or elements omitted. The disclosure includes substitutions or combinations of components and / or elements between one embodiment and other embodiments. The scope of the disclosure is not limited to the description of the embodiments. Several technical scopes of the disclosure are shown by the description in the claims and should be understood to further include all modifications within the same meaning and scope as the description in the claims.

[0110] In the above-described embodiments, the dedicated computer constituting the motion estimation device is a positioner ECU 100. Alternatively, the dedicated computer constituting the motion estimation device may be a driving control ECU mounted on vehicle A, or an actuator ECU that independently controls the driving actuators of vehicle A. Alternatively, the dedicated computer constituting the motion estimation device may be a navigation ECU. Alternatively, the dedicated computer constituting the motion estimation device may be an HCU (HMI (Human Machine Interface) Control Unit) that controls the information display of an information display system. Furthermore, the dedicated computer constituting the motion estimation device may be a server device located outside vehicle A. Additionally, the dedicated computer constituting the motion estimation device may be a structure that estimates the motion amount in a mobile body other than vehicle A, such as a mobile robot.

[0111] In the above embodiment, the locator ECU 100 outputs the current position of vehicle A estimated based on the calculated movement. Alternatively, the locator ECU 100 may be configured to output the movement without estimating the current position. In this case, the device that outputs the destination, such as the on-board unit 90, can estimate the current position based on the movement. Furthermore, the locator ECU 100 may also output satellite positioning results in addition to the movement.

[0112] In the above embodiment, the priority determination unit 130 calculates the priority as the sum of multiple indicators. Alternatively, the priority determination unit 130 may also calculate the priority as the product of multiple indicators. Or, the priority determination unit 130 may remove any combination of multiple indicators that falls within a predetermined non-allowable range from the combination used for movement calculation.

[0113] In the above embodiment, the priority determination unit 130 calculates the satellite quality index based on the number of signals with cycle slips. Alternatively, the priority determination unit 130 may set a higher satellite quality index if the average carrier-to-noise ratio of each satellite is higher. Or, the priority determination unit 130 may set the satellite quality index based on multiple parameters including the number of signals with cycle slips and the average carrier-to-noise ratio.

[0114] In the above-described embodiment, the priority determination unit 130 calculates reliability based on the estimation error of past positions or the entropy of the point group. Alternatively, in a structure where its own position estimation uses a conventional RTK, the priority determination unit 130 may set a higher reliability if the number of satellites used in a conventional RTK is greater. Alternatively, the priority determination unit 130 may set a higher reliability if the ratio obtained from a ratio test is higher. Alternatively, the priority determination unit 130 may set the reliability based on the multiple parameters described above.

[0115] In the first embodiment described above, the simplified positioning unit 120 calculates the travel distance using dead reckoning. Alternatively, the simplified positioning unit 120 may also calculate the travel distance based on satellite positioning results such as Time Difference Kinematics (RTK), which will be described later.

[0116] The positioner ECU100 can also be configured as a dedicated computer that includes at least one of digital circuitry and analog circuitry as a processor. Specifically, digital circuitry refers to at least one of the following: ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), and CPLD (Complex Programmable Logic Device). Furthermore, such digital circuitry may also include a memory storing a program.

[0117] The positioner ECU 100 can be provided by a computer or by a group of computer resources linked using a data communication device. For example, some of the functions provided by the positioner ECU 100 in the above embodiments can also be implemented by other ECUs or server devices.

Claims

1. A mobility estimation device that uses time difference-of-kinematic (RTK) to estimate the mobility of a mobile body equipped with a receiver from a past location to a current location, wherein the receiver receives satellite data from positioning satellites, and the mobility estimation device comprises: The priority determination unit determines the priority level to be used in the estimation of the mobility, based on the combination of past satellite data acquired by the receiver at multiple past locations and current satellite data acquired at the current location; and The movement calculation unit calculates the relative movement from the past location to the current location based on the combination of the above-mentioned combinations, where the priority is higher than the low priority range of the above-mentioned combinations.

2. The mobility estimation device according to claim 1, wherein, The greater the difference between the distance the moving body traveled from the reference point to the past location and the distance it traveled from the reference point to the current location, the higher the priority of the combination.

3. The mobility estimation device according to claim 1, wherein, The smaller the elapsed time from the time the aforementioned satellite data was acquired in the past to the time the aforementioned satellite data was acquired now, the higher the priority of the aforementioned combination.

4. The mobility estimation device according to claim 1, wherein, The higher the quality of the above-mentioned satellite data, the higher the priority of the above-mentioned combination.

5. The mobility estimation device according to claim 1, wherein, The higher the reliability of the estimated location of the aforementioned past locations, the higher the priority of the aforementioned combination.

6. The mobility estimation device according to any one of claims 1 to 5, wherein, The aforementioned priority determination department determines the priority based on multiple indicators and assigns weights to each of the aforementioned indicators according to the conditions.

7. The mobility estimation device according to claim 6, wherein, The priority determination unit uses the difference between the distance the moving body travels from the reference point to the past location and the distance it travels from the reference point to the current location as a distance difference index and includes it in the index. The smaller the moving speed of the moving body, the smaller the weight of the distance difference index is set by the priority determination unit.

8. A mobility estimation method, executed by a processor, for estimating the mobility of a mobile body equipped with a receiver from a past location to a current location using time difference of kinematics (RTK), wherein the receiver receives satellite data from positioning satellites, the mobility estimation method comprising: The priority determination process determines the priority level to be used in the estimation of the amount of movement by considering the combination of past satellite data acquired by the receiver at multiple past locations and current satellite data acquired at the current location. as well as The movement calculation process calculates the relative movement from the past location to the current location based on the combination of the above-mentioned combinations, where the priority is higher than the lower priority range of the above-mentioned combinations.

9. The mobility estimation method according to claim 8, wherein, In the priority determination process described above, the combination with the larger the difference between the moving distance of the moving body from the reference point to the past point and the moving distance from the reference point to the current point, the higher the priority.

10. The mobility estimation method according to claim 8, wherein, In the above priority determination process, the combination with the shorter the elapsed time from the previous acquisition time of the above satellite data to the current acquisition time of the above satellite data, the higher the priority.

11. The mobility estimation method according to claim 8, wherein, In the aforementioned priority determination process, the higher the quality of the aforementioned satellite data in the past, the higher the priority of the aforementioned combination.

12. The mobility estimation method according to claim 8, wherein, In the above priority determination process, the higher the reliability of the estimated location in the above past locations, the higher the priority of the above combination.

13. The mobility estimation method according to any one of claims 8 to 12, wherein, In the above priority determination process, the priority is determined based on multiple indicators, and weights are assigned to each of the above indicators according to the conditions.

14. The mobility estimation method according to claim 13, wherein, In the aforementioned priority determination process, the difference between the distance the moving body travels from the reference point to the past point and the distance the moving body travels from the reference point to the current point is used as a distance difference index and included in the index. Furthermore, the smaller the moving speed of the moving body, the smaller the weight of the distance difference index is set.

15. A storage medium storing a motion estimation program containing multiple commands, wherein a processor executes the multiple commands to estimate the motion of a mobile body equipped with a receiver from a past location to a current location using time difference-of-kinematic (RTK) data, wherein the receiver receives satellite data from positioning satellites. The above commands include: The priority determination process involves determining the priority level to be used in the estimation of the aforementioned mobility, based on a combination of past satellite data acquired by the receiver at multiple past locations and current satellite data acquired at the current location; and The movement calculation process calculates the relative movement from the past location to the current location based on the combination of the above-mentioned combinations, where the priority is higher than the lower priority range of the above-mentioned combinations.

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