Environmental mapping apparatus, environmental mapping method, and program

Abstract

This environmental map production device performs environmental map production efficiently and improves the quality an environmental map by including: a calculating unit configured to calculate evaluation values of the effectiveness of positioning solutions based on data measured, using a GNSS, by vehicles that travel in a certain area at a plurality of time periods, for each of a plurality of segments that divide the area; and a sorting unit that is configured to sort the positioning solutions to be used in the environmental map production and the positioning solutions not to be used in the environmental map production, on the basis of the evaluation values.

Description

TECHNICAL FIELD

[0001] The present invention relates to an environment map production apparatus, an environment map production method, and a program. BACKGROUND ART

[0002] In the autonomous traveling of autonomous vehicles and autonomous mobile robots, it is necessary to accurately estimate the position and orientation (azimuth) of a moving vehicle in real time for vehicle control. Accordingly, a method of estimating the self-position of a vehicle by using a pre-prepared environment map around a traveling area and performing scan matching on spatial information data of the surrounding environment obtained by LiDAR, a camera, Radar, or the like with the environment map has been studied. As typical scan matching algorithms, ICP (Iterative Closest Point) and NDT (Normal Distributions Transform) are known. The production process of an environment map used for such applications is different from the production process of a general two-dimensional (plane) map.

[0003] In the production of a two-dimensional map using aerial photographs, after aerial photographs of a production target area are captured and the captured images are corrected into orthoimages, a plurality of ground control points (GCP) are set in the production target area to calibrate the absolute position of the image. Position measurement of ground control points is performed by surveying combining optical distance measuring means such as Global Navigation Satellite Systems (GNSS) and total stations. Although such a method is effective for efficiently creating a two-dimensional map, it has a problem that detailed three-dimensional spatial information near the ground, which is used for self-position estimation of autonomous vehicles such as autonomous driving vehicles and autonomous mobile robots, cannot be obtained.

[0004] One method for collecting spatial information from a road perspective used by autonomous vehicles is a measurement method using a dedicated vehicle-mounted surveying system called MMS (Mobile Mapping System). The MMS is equipped with navigation sensors to measure the vehicle's position and attribute sensors to collect spatial information data of the surrounding environment.

[0005] Navigation sensors include a GNSS signal receiver for measuring absolute position, as well as relative positioning means such as an inertial measurement unit (IMU) and odometry. Vehicle position is measured through composite positioning by coupling these data. Attribute sensors include devices such as laser scanners and cameras, which collect data as a collection of points with coordinate values ​​called a point cloud. In some cases, colored point cloud data is generated using image information from the camera.

[0006] Environmental maps used include point cloud maps themselves, or vector maps created by extracting feature data from point cloud maps to reduce data size. Feature data includes road sidings, road centerlines, lane boundaries, pedestrian crossings, road signs, and guardrails. Environmental map data used by autonomous vehicles requires absolute positional accuracy at the level of a few centimeters to tens of centimeters, enabling lane determination during vehicle self-localization. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Kiichiro Ishikawa, "On Road Surveying Using a Mobile Mapping System," Journal of the Japan Society for Precision Engineering, Vol. 79, No. 5, 2013, pp. 397-400. [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] Regarding the collection of data for environmental mapping using MMS, measuring vehicle positions in areas where GNSS positioning is difficult presents a challenge. In urban canyons, a type of reception environment in cities, satellite signals are blocked by buildings and other structures around the GNSS antenna. This reduces the number of visible satellites that can receive satellite signals directly, and the reception of multipath signals caused by reflection and diffraction of satellite signals by structures degrades GNSS positioning accuracy. Furthermore, in carrier phase positioning, cycle slip occurs, where the continuous acquisition of the carrier phase is interrupted.

[0009] In these environments, GNSS positioning solutions depend on the relative positions of satellites and structures, resulting in errors that are not distributed normally around zero. Therefore, accurately estimating the true value using extended Kalman filters, etc., is difficult. This can lead to failures in MMS measurement work and the need for rework. Furthermore, when creating maps from point cloud data containing errors in the measured position data, correction is time-consuming. In particular, in urban canyon environments, effective positioning solutions are often not obtained over a wide area, making correction work difficult and sometimes requiring correction using conventional optical surveying methods.

[0010] Thus, in producing environmental maps using MMS, there are challenges regarding production costs and quality in urban commercial areas where demand is high.

[0011] This invention has been made in view of the above points, and aims to efficiently produce environmental maps and improve the quality of environmental maps. [Means for solving the problem]

[0012] Therefore, to solve the above problems, the environmental mapping device uses positioning solutions based on data measured using GNSS by vehicles traveling in a certain area at multiple time periods. Regarding whether it is an effective solution close to the true value.The system includes a calculation unit configured to calculate an evaluation value for each of the multiple segments that divide the area, and a selection unit configured to select, based on the evaluation value, the positioning solutions to be used in the production of the environmental map and the positioning solutions not to be used in the production of the environmental map. [Effects of the Invention]

[0013] The objective is to efficiently produce environmental maps and improve their quality. [Brief explanation of the drawing]

[0014] [Figure 1] This figure shows an example of the configuration of a mapmaking system according to an embodiment of the present invention. [Figure 2] This figure shows an example configuration of the data acquisition device 20 in an embodiment of the present invention. [Figure 3] This figure shows an example configuration of the self-position estimation device 30 in an embodiment of the present invention. [Figure 4] This figure shows an example of the hardware configuration of the map creation and distribution server 10 in an embodiment of the present invention. [Figure 5] This figure shows an example of the functional configuration of the map creation and distribution server 10 in an embodiment of the present invention. [Figure 6] This diagram illustrates the segments used to divide the area for creating the environmental map. [Figure 7] This diagram illustrates the method for evaluating the GNSS positioning suitability of a segment. [Figure 8] This is a diagram illustrating the procedure for validating the effectiveness of GNSS positioning solutions. [Figure 9] This diagram illustrates the verification process based on the height value of the GNSS positioning solution when no GNSS positioning solution exists on the road. [Figure 10] This figure illustrates the test based on consistency evaluation with IMU24, Odometry26, and EDR25 data. [Modes for carrying out the invention]

[0015] In the present embodiment, a sufficient amount of data is collected in a plurality of different time periods in an area where an environment map is to be produced, and data is sorted out through statistical processing based on positioning results, thereby improving the work efficiency of environment map production and improving the quality of the environment map. In addition, by delivering information related to the expected value of GNSS positioning accuracy together with environment map data, the reliability of self-localization estimation operation for autonomous driving is improved.

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0017] [Configuration] FIG. 1 is a diagram showing a configuration example of a map production system according to an embodiment of the present invention. In FIG. 1, the map production system includes one or more data collection devices 20, a map production and distribution server 10, and one or more self-localization estimation devices 30. The data collection device 20 and the self-localization estimation device 30 are connected to the map production and distribution server 10 via a communication network.

[0018] The data collection device 20 is a device for collecting data for environment map production, and is mounted on a measurement vehicle. The data collection device 20 uploads the collected data to the map production and distribution server 10.

[0019] The map production and distribution server 10 is one or more computers that generate map data of an environment map based on data uploaded from the data collection device 20. The map production and distribution server 10 distributes the generated map data to the self-localization estimation device 30.

[0020] The self-localization estimation device 30 is a device for performing self-localization estimation using an environment map, and is mounted on an autonomous vehicle such as an autonomous driving car or an autonomous mobile robot.

[0021] For environmental maps, point cloud maps themselves or vector maps (which are created by extracting feature data from point cloud maps to reduce data size) are used. Feature data includes road sidings, road centerlines, lane boundaries, pedestrian crossings, road signs, and guardrails.

[0022] Figure 2 shows an example of the configuration of a data acquisition device 20 in an embodiment of the present invention. In Figure 2, the data acquisition device 20 includes a GNSS antenna 21, a GNSS receiver 22, a laser scanner 23, an IMU (Inertial Measurement Unit) 24, an EDR (Event Data Recorder) 25, an odometry unit 26, a clock unit 27, a data storage unit 28, and a communication unit 29, etc.

[0023] The GNSS receiver 22 outputs observation data of GNSS satellite signals received by the GNSS antenna 21 and data related to the GNSS positioning status. The observation data of GNSS satellite signals includes data such as the received signal strength, pseudo-distance, carrier phase, and Doppler frequency of each satellite signal. The data related to the positioning status includes data such as error ellipse and cycle slip.

[0024] The laser scanner 23 is a device that irradiates surrounding objects with a laser to measure the distance to those objects with high precision, and is also known as LiDAR (Light Detection and Ranging).

[0025] The IMU24 is a device composed of an accelerometer, gyroscope, magnetic sensor, and other components.

[0026] The EDR25 is a device that records the driving operations of a measurement vehicle, such as acceleration, braking, and steering.

[0027] Odometry 26 is a device that calculates vehicle speed data from the rotation speed of the wheels of the vehicle being measured.

[0028] The clock unit 27 synchronizes with GNSS satellite signals and supplies highly accurate time information for measuring the time of data measurement in each of the devices: laser scanner 23, IMU 24, EDR 25, and odometry 26.

[0029] The communication unit 29 is a communication interface used when uploading data to the map production and distribution server 10 via the communication network. The communication unit 29 uses V2X (Vehicle to X) communication methods such as mobile communication, wireless LAN (Local Area Network), and DRSC (Dedicated Short-Range Communications).

[0030] The measured data may be uploaded in real time while the measurement vehicle is in motion, or it may be uploaded in a batch after a certain period of data has been collected in the data storage unit 28. For example, if GNSS observation data is transmitted in real time, the RTCM (Radio Technical Commission for Maritime Services) format is used, and if it is transmitted in a batch, the RINEX (Receiver Independent Exchange Format) data format is used.

[0031] In addition to the laser scanner 23, the data acquisition device 20 may also be equipped with a camera for detecting and identifying feature data.

[0032] Data collection in the same area may be carried out by taking multiple measurements at different time periods using the same measurement vehicle, or by operating multiple measurement vehicles at different time periods.

[0033] Figure 3 shows an example of the configuration of a self-position estimation device 30 in an embodiment of the present invention. In Figure 3, the self-position estimation device 30 includes a GNSS antenna 31, a GNSS receiver 32, a laser scanner 33, a self-position estimation unit 34, a data output unit 35, a data storage unit 36, and a communication unit 37, etc.

[0034] The GNSS receiver 32 uses the GNSS satellite signals received by the GNSS antenna 31 to perform positioning calculations and outputs the approximate position of the autonomous vehicle in real time.

[0035] The self-position estimation unit 34 searches the surrounding environment map of the approximate position output from the GNSS receiver 32 and estimates its own position by scanning and matching the point cloud data of the surrounding environment acquired by the laser scanner 33 with the environment map.

[0036] The data output unit 35 outputs the coordinate data obtained as a result of self-position estimation in a format required by the control device of the autonomous vehicle. The basic principle of controlling the autonomous vehicle is to drive along a pre-set (planned) route. The control device controls the autonomous vehicle to minimize the difference between the result of self-position estimation and the set (planned) route. The control device also performs obstacle detection, signal recognition, route setting, and route re-setting for obstacle avoidance.

[0037] The communication unit 37 is a communication interface for downloading environmental map data and data including the GNSS positioning suitability described later from the map production and distribution server 10. The communication unit 37 uses mobile communication methods, wireless LAN methods, and V2X communication methods such as DRSC.

[0038] The data storage unit 36 ​​stores downloaded environmental maps and other data.

[0039] The autonomous vehicle may be equipped with a camera and a radar instead of (or in addition to) the laser scanner 33. In that case, its own position is estimated by scanning and matching point cloud data generated from the output data of the camera and radar with an environmental map. The self-position estimation device 30 may also be equipped with a combined positioning means such as GNSS / IMU as a backup self-position estimation means in case the laser scanner 33 or the self-position estimation unit 34 fails.

[0040] Figure 4 shows an example of the hardware configuration of a map creation and distribution server 10 in an embodiment of the present invention. The map creation and distribution server 10 in Figure 4 includes a drive device 100, an auxiliary storage device 102, a memory device 103, a processor 104, and an interface device 105, etc., which are all interconnected by bus B.

[0041] The program that enables processing on the map creation and distribution server 10 is provided on a recording medium 101 such as a CD-ROM. When the recording medium 101 containing the program is set in the drive device 100, the program is installed from the recording medium 101 to the auxiliary storage device 102 via the drive device 100. However, the program does not necessarily have to be installed from the recording medium 101; it may also be downloaded from another computer via a network. The auxiliary storage device 102 stores the installed program as well as necessary files and data.

[0042] The memory device 103 reads and stores a program from the auxiliary storage device 102 when a program startup command is received. The processor 104 is either a CPU or a GPU (Graphics Processing Unit), or both a CPU and a GPU, and executes functions related to the map creation and distribution server 10 according to the program stored in the memory device 103. The interface device 105 is used as an interface for connecting to a network.

[0043] Figure 5 shows an example of the functional configuration of a map production and distribution server 10 in an embodiment of the present invention. In Figure 5, the map production and distribution server 10 includes a data receiving unit 11, a positioning calculation unit 12, a suitability determination unit 13, a validity verification unit 14, a map production unit 15, and a map data distribution unit 16. Each of these units is realized by processing that one or more programs installed on the map production and distribution server 10 cause the processor 104 to execute. The map production and distribution server 10 also utilizes a data storage unit 17. The data storage unit 17 can be realized using, for example, an auxiliary storage device 102, or a storage device that can be connected to the map production and distribution server 10 via a network.

[0044] [Operation] The operation of this embodiment consists of (1) a data collection step for map production, (2) a map production step, (3) a map data distribution step, and (4) a map data update step.

[0045] (1) The data collection process for map production is the process of collecting data necessary for map production in the field using a measurement vehicle. (2) The map production process is the process in which the map production and distribution server 10 produces an environmental map. (3) The map data distribution process is the process in which the map production and distribution server 10 distributes the environmental map data to autonomous vehicles. (4) The map data update process is the process of updating the environmental map.

[0046] The operation of each process will be explained in detail below.

[0047] (1) Data collection process for mapmaking The data collection process for mapmaking involves collecting data using a data collection device 20 mounted on a measurement vehicle. The measurement vehicle repeatedly travels within the area for which the environmental map is to be produced, collecting data at different times (timings) near the same measurement points. For this reason, the data collection device 20 may be mounted not only on a dedicated measurement vehicle but also on commercial vehicles such as route buses, shuttle buses, or taxis that circulate within a specific area. Furthermore, there may be more than one vehicle equipped with the data collection device 20 to collect data for the same area.

[0048] The acquisition time of data obtained by the laser scanner 23 (LiDAR), IMU 24, EDR 25, and odometry 26 is marked with a timestamp based on the time information of the clock unit 27, which is highly synchronized with the GNSS signal, i.e., Coordinated Universal Time (UTC). In addition, the GNSS receiver 22 outputs observation data and data related to the GNSS positioning status, which are time-synchronized with the GNSS satellite signal. An ID is assigned to the above measurement data to identify the measurement vehicle.

[0049] The data collected by the data storage unit 28 may be uploaded in real time to the map creation and distribution server 10 by the communication unit 29 while the vehicle is in motion. Alternatively, the data may be temporarily stored in the data storage unit 28 and then uploaded to the map creation and distribution server 10 in a batch, or output to another medium.

[0050] The data uploaded by the communications unit 29 is received by the data receiving unit 11 of the map production and distribution server 10. The data receiving unit 11 records the received data in the data storage unit 17.

[0051] (2) Mapmaking process The map creation process is a process in which, after measurement, the map creation and distribution server 10 creates an environmental map using the data collected by the measurement vehicle (data recorded in the data storage unit 17). In this process, first, the positioning calculation unit 12 performs positioning calculation processing (hereinafter referred to as "GNSS positioning calculation") using the observation data collected by the GNSS receiver 22. The GNSS positioning calculation is performed using a carrier phase (interference) positioning method. As for the carrier phase positioning method, the RTK (Real Time Kinematic)-GNSS positioning method, which uses the observation data of the reference station as correction information for Observation Space Representation (OSR), the PPP (Precise Point Positioning) method, which does not use the observation data of the reference station and uses correction information for State Space Representation (SSR), or the PPP-AR (Precise Point Positioning Ambiguity Resolution) method are used.

[0052] In an ideal reception environment where no structures, trees, or other obstructions block satellite signals around the receiving position, the GNSS positioning solution obtained by the carrier phase positioning method yields an error (positioning accuracy) on the order of a few centimeters relative to the true value (receiving position). On the other hand, if obstructions exist around the receiving position, the sky region in which satellite signals can be received as direct waves is limited, reducing the number of visible satellites. In addition, the reception of multipath signals generated by reflection and diffraction of satellite signals by the obstructions degrades positioning accuracy, resulting in errors of several meters, and in some cases tens of meters or more, in the GNSS positioning solution. Errors (noise) caused by such reception environments depend on the spatial position of the obstructions and do not follow a normal distribution with the true value (receiving position) as zero (center). In other words, the error is not normal white noise. For this reason, it becomes difficult to estimate the true value in composite positioning, where GNSS positioning solutions are coupled with IMU24 and odometry26 data using extended Kalman filters, etc. In short, it becomes difficult to secure data with the absolute position accuracy necessary for mapmaking.

[0053] For the reasons stated above, (1) not all data collected in the mapmaking data collection process is used for mapmaking, and the data must be appropriately selected. In the mapmaking process of this embodiment, whether the GNSS positioning solution collected by the measurement vehicle in the mapmaking data collection process for a certain time epoch is a valid solution close to the true value of the reception position that can be used for mapmaking is determined by the following procedures: (A) GNSS positioning suitability determination and (B) GNSS positioning solution validity test.

[0054] (A) GNSS positioning suitability assessment When determining the effectiveness of a GNSS positioning solution obtained as a result of GNSS positioning calculation (i.e., whether it is an effective solution close to the true value of the reception position), it is not always easy to determine the effectiveness of individual GNSS positioning solutions. Therefore, as shown in Figure 6, the suitability determination unit 13 divides the environmental map production area into multiple segments and ranks the suitability of GNSS positioning (the degree to which an effective solution is expected to be obtained: hereinafter referred to as "GNSS positioning suitability") for each segment by statistical evaluation using multiple (numerous) data collected at different time periods. The segments may be divided in a grid pattern in the target area of ​​the environmental map as shown in Figure 6(a), or they may be divided at regular intervals along the roads on which autonomous vehicles travel within the target area as shown in Figure 6(b).

[0055] Regarding GNSS satellite signals, with the exception of some geostationary satellites, the satellite's position as seen from the receiving location orbits the sky over time. For example, GPS (Global Navigation System) satellites return to almost the same position in the sky in approximately 24 hours. In a receiving environment where structures exist around the receiving location, the relative positional relationship between the satellite and the structures changes over time, and consequently, the GNSS positioning solution at a given receiving location also changes over time. Therefore, by statistically evaluating a large amount of data measured at different time periods, it is possible to estimate the magnitude of the positioning error (expected value of statistical error) caused by structures (receiving environment).

[0056] The following methods can be applied to classify each divided segment into classes based on GNSS positioning suitability, including the method of evaluating the distribution of measurement data as described above. One of these methods, or a combination of several methods, can be used to classify (rank) each segment.

[0057] (i) Evaluation by plotting GNSS positioning solutions The suitability determination unit 13 plots the GNSS positioning solution data on a two-dimensional map. To prevent the mixing of data from driving on adjacent roads, the driving plan of the measurement vehicle may be referenced from the vehicle ID and measurement time of the measurement data before plotting the data. Based on the data thus plotted, the suitability determination unit 13 classifies the GNSS positioning suitability. Here, the purpose is to evaluate the distribution state (degree of variability) of the plot positions of the entire number of data, rather than the plot position of individual data, so the absolute positional accuracy of the two-dimensional map used for plotting is not a concern.

[0058] In an ideal reception environment with no obstructions around the receiving position (antenna position), the plotted GNSS positioning solutions are expected to be distributed within the width of the road traveled by the measurement vehicle, excluding the accuracy limits (errors) inherent in the GNSS positioning method. In the carrier phase positioning method, the positioning error in an ideal reception environment is only a few centimeters, so the GNSS positioning solutions are expected to be located near the actual trajectory of the vehicle (the true value of the receiving position).

[0059] On the other hand, in suboptimal reception environments where obstacles such as buildings exist around roads, GNSS positioning solutions deviate from the true value. As the error in GNSS positioning solutions changes with the change in satellite position over time, the distribution (magnitude of variation) of numerous GNSS positioning solutions measured at different times can be used to evaluate the degree to which GNSS positioning accuracy in a given segment is affected by the reception environment, i.e., the suitability of that segment for GNSS positioning.

[0060] As an example of the evaluation method, the suitability determination unit 13 quantitatively evaluates the distribution of GNSS positioning solutions in the direction perpendicular to the road in the measurement area, as shown in Figure 7. The same applies when a sidewalk is used as the measurement area. That is, if the GNSS positioning solution contains errors, the errors are distributed in all directions within a two-dimensional plane, but since there is the fact that the vehicle equipped with the data acquisition device 20 traveled on a road (or sidewalk) (information about the true value), the errors can be estimated by measuring the distribution of positioning solutions in the direction perpendicular to the road.

[0061] Note that Figure 7 corresponds to the case where the segments are divided as in Figure 6(b), but even when the segments are divided as in Figure 6(a), the point of quantitatively evaluating the distribution state of GNSS positioning solutions in the direction perpendicular to the road remains the same. Furthermore, the quantitative evaluation value of the distribution state is, for example, the maximum deviation amount from the road center (center line) of the positioning solution in the direction perpendicular to the road, or the RMS (Root Mean Square) value. In this case, the larger the evaluation value, the lower the GNSS positioning suitability is evaluated to be.

[0062] (ii) Evaluation by plotting point cloud data The suitability determination unit 13 uses the GNSS positioning solution and the point cloud data of the structure obtained by the laser scanner 23 to evaluate the distribution of measurement results of measurement points (locations where the data acquisition device 20 was located) when the point cloud data of the same structure is matched (overlaid). The suitability determination unit 13 may, similar to (i), plot the data on a two-dimensional map after referring to the driving plan of the measurement vehicle from the vehicle ID and measurement time of the measurement data so as not to mix data from driving on adjacent roads. If the accuracy of the GNSS positioning solution is high, the distribution of measurement points should approach the trajectory of the measurement vehicle, but the greater the degree of variation of the position of the measurement points from the road width, the lower the GNSS positioning suitability of the segment can be evaluated. The suitability determination unit 13 evaluates the distribution of measurement points using the same logic as the GNSS positioning solution in (i) and classifies the positioning suitability of each segment.

[0063] (iii) Evaluation by simulation The suitability determination unit 13 estimates the reception characteristics of GNSS satellite signals in a given segment through simulation. Specifically, possible methods include using 3D map data including building height information and publicly available GNSS satellite orbit information to estimate the DOP (Dilution of Precision) value of visible satellite signals at reception positions within each segment, or estimating the generation of multipath signals due to structures around reception positions within each segment using 3D ray tracing simulation. Based on the results of simulations over time, the suitability determination unit 13 classifies the GNSS positioning suitability class of each segment. Note that a larger DOP value indicates lower GNSS positioning suitability. Also, a stronger multipath signal indicates lower GNSS positioning suitability.

[0064] (iv) Evaluation using output data from GNSS receiver 22 The suitability determination unit 13 classifies each segment into classes using data such as error ellipses, cycle slip frequency, the ratio of converged (FIX) solutions of the RTK-GNSS positioning calculation in the positioning calculation unit 12, and the ratio of residual values ​​between the first and second solutions of the LAMDA (Least-squares Ambiguity Decorrelation Adjustment) method as positioning status data output by the GNSS receiver 22. The larger the diameter of the error ellipses and the higher the cycle slip frequency, and the smaller the FIX solution ratio and Ratio value, the lower the GNSS positioning suitability of that segment is evaluated.

[0065] Based on the above, the suitability determination unit 13 classifies each segment of the area to be produced for environmental map production into two or more classes based on its GNSS positioning suitability. As a result, segments are selected (distinguished) into those for which all GNSS positioning solutions are used in the production of the environmental map and those for which only some GNSS positioning solutions are used in the production of the environmental map. For example, a threshold for classifying (ranking) the GNSS positioning suitability is set in advance for an evaluation value based on one or more of the evaluation methods (i) to (iv) above. The suitability determination unit 13 classifies the GNSS positioning suitability of each segment into a class by comparing the evaluation value with the threshold.

[0066] The reception environment with the highest GNSS positioning suitability class is an open-sky reception environment with virtually no obstructions. The reception environment with the lowest GNSS positioning suitability class is, for example, a deep urban canyon reception environment, or reception environments near tunnel entrances and exits, or under elevated structures, where tall buildings surround roads and the area of ​​open space where satellite signals can be received directly is severely limited. The convergence (FIX) solution of GNSS positioning in the segment with the highest GNSS positioning suitability is considered to have a high probability of being an effective solution close to the true value.

[0067] (B) Validity testing of GNSS positioning solutions Based on the classification results of the GNSS positioning suitability class for each segment of the area to be mapped, performed by the suitability determination unit 13, data collected in segments with high GNSS positioning suitability are determined to have valid GNSS positioning solutions and are used for map production. On the other hand, for data from segments with low GNSS positioning suitability, the effectiveness verification unit 14 verifies the effectiveness of each GNSS positioning solution (whether the GNSS positioning solution is a valid solution close to the true value) using the procedure shown below, and only data that meets the criteria are used for map production.

[0068] Figure 8 illustrates the procedure for validating the effectiveness of GNSS positioning solutions. As mentioned above, the error in GNSS positioning solutions caused by the reception environment does not exhibit normality, so applying statistical outlier testing methods is not always effective. Therefore, the effectiveness testing unit 14 performs a test by comparing with a reference value (expected value) and selects some GNSS positioning solutions as valid solutions. This is explained in detail below.

[0069] (S101) Verification based on the height value of the GNSS positioning solution If 3D map data is available (if 3D map data is prepared separately in advance), the validity verification unit 14 uses the road surface height (elevation) information at the 2D position (latitude, longitude) of the GNSS positioning solution as the true value and uses it as the criterion for verification. It rejects data where the deviation between the road surface height value of the map data, corrected considering the position of the GNSS antenna from the road surface of the measuring vehicle, and the height (elevation) value of the GNSS positioning solution is greater than a threshold (for example, 30 cm).

[0070] If a GNSS positioning solution does not exist on the road, it is likely not a valid solution and may be rejected. However, as shown in Figure 9, the discrepancy between the road surface height and the height value of the GNSS positioning solution may be evaluated using the map data value of the road centerline closest to the GNSS positioning solution. Alternatively, the offset of the height data of the 3D map data may be corrected by comparing it with the converged (FIX) solution of the GNSS positioning solution in the segment with the highest GNSS positioning suitability. This is because the converged (FIX) solution in the segment with the highest GNSS positioning suitability is considered to have a high probability of being a valid solution close to the true value.

[0071] (S102) Verification based on landmark location information If data of roadside landmarks such as manholes, utility poles, streetlights, traffic lights, and signs with high-precision 3D positional information is available, the effectiveness verification unit 14 uses the landmark location as the true value as the criterion for verification and rejects data where the discrepancy between the landmark location calculated from the GNSS positioning solution and the point cloud data from the laser scanner 23 and the corresponding location in the landmark location information data is greater than a threshold (e.g., 30 cm). Alternatively, the offset of the landmark location information data may be corrected by comparing it with the landmark location measurement value from the point cloud data using the converged (FIX) solution of the GNSS positioning solution in the segment with the highest GNSS positioning suitability.

[0072] (S103) Testing by evaluating consistency with IMU24, Odometry26, and EDR25 data. The effectiveness verification unit 14 treats data from the same measurement vehicle (ID) taken at different time intervals (for example, data acquired on the same day) as the same data stream and extracts base point data from the GNSS positioning solutions in the data stream. The base point data is extracted from the data of the segment with the highest GNSS positioning suitability class (for example, segment B in Figure 10) that is closest to the segment to be tested (for example, segment A and segment C in Figure 10) based on the GNSS positioning suitability judgment, using the carrier phase positioning method at the position closest to the segment to be tested. The convergence (FIX) solution in the segment with the highest GNSS positioning suitability is considered to have a high probability of being an effective solution close to the true value.

[0073] In Figure 10, two base points are shown on both the Segment A side and the Segment C side of Segment B. Of these base points, the base point included in the lower lane in the figure is the base point for driving (moving) to the left in the figure, and the base point included in the upper lane in the figure is the base point for driving (moving) to the right in the figure.

[0074] The effectiveness verification unit 14 determines whether the GNSS positioning solution for the segment under verification is a valid solution (selection of a valid solution from the GNSS positioning solutions for the segment under verification) by evaluating whether it is consistent with the position estimated by integrating the relative displacement measured by the IMU 24, the integrated value of vehicle speed data measured by the odometry 26, and the steering angle from the EDR 25 data, which are measured from the coordinate values ​​of the base point data (estimated value of the movement path of the measured vehicle) (whether the degree of deviation (difference) is below a threshold). The effectiveness verification unit 14 performs the same procedure for movement paths in the opposite direction to the direction of travel of the vehicle (direction of time progression) (Figure 10). Alternatively, it traces the movement path from the GNSS positioning solution under verification in the direction of travel or the opposite direction to determine the position at the base point, and evaluates the deviation between the determined position and the positioning solution at the base point. In addition to the data from IMU24, odometry26, and EDR25 used for consistency evaluation, data from LIO (LiDAR Inertial Odometry) based on the measurement data of the laser scanner23 may also be used as data for the relative displacement measurement means. A GNSS positioning solution that is determined to be a valid solution in either the direction of vehicle movement (the direction in which time progresses) or the opposite direction will ultimately be determined to be a valid solution.

[0075] The effective GNSS positioning solution may be selected by performing all of the above steps S101 to S103, or the effective GNSS positioning solution may be selected by performing any one or two of the steps.

[0076] The above is the procedure for selecting (extracting) effective GNSS positioning solutions.

[0077] The map production unit 15 performs a composite positioning calculation in the forward direction (the direction in which time progresses) and the reverse direction (the direction opposite to the direction in which time progresses) using the effective GNSS positioning solutions selected by the suitability determination unit 13 and the effectiveness verification unit 14, along with data from the IMU 24 and odometry 26. Here, data from LIO (LiDAR Inertial Odometry) may be used as the relative positioning means. The composite positioning calculation is performed using tight coupling or loose coupling with an Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter, etc.

[0078] The class value of the GNSS positioning suitability of the segment to which the GNSS positioning solution belongs is used to determine the contribution (weighting, gain) of the GNSS positioning solution in the composite positioning calculation. For example, the map production unit 15 performs composite positioning calculations in segments with high GNSS positioning suitability by giving a larger weight to the GNSS positioning solution (evaluating the reliability of the GNSS positioning solution and increasing its contribution). In addition, for time epochs in segments with low GNSS positioning suitability where no valid GNSS positioning solution that has passed the verification exists, the map production unit 15 performs composite positioning calculations using forward and reverse dead reckoning (DR) with valid GNSS positioning solutions from neighboring time epochs in the same data stream and IMU, odometry (and LIO) data.

[0079] The map production unit 15 uses the composite positioning solution obtained through the above procedure and the point cloud data obtained by the laser scanner 23 to produce an environmental map (generate map data for the environmental map). In the process of generating point cloud map data, noise data such as other vehicles and pedestrians is removed, and the map data obtained by extracting only the data of permanent structures such as buildings is appropriately corrected for distortion and other issues.

[0080] In this embodiment, by selecting effective solutions from GNSS positioning solutions, the distortion of the generated point cloud map data is reduced, thereby reducing the correction work. The map production unit 15 may also perform correction of the map extracted from the point cloud data using loop closure processing. The map production unit 15 may also perform calibration, including absolute position accuracy, on the environmental map extracted from the point cloud data using existing high-precision 2D map data. The final environmental map output may be point cloud map data or a vector map obtained by extracting feature data from the point cloud map and reducing the data size.

[0081] By following the above procedure, it is possible to improve the efficiency of environmental map data production and enhance the quality of environmental map data by discarding data with low effectiveness as GNSS positioning solutions.

[0082] (3) Map data distribution process The map data distribution unit 16 of the map production and distribution server 10 distributes the environmental map data produced in the map production process, along with the GNSS positioning suitability data obtained in the map production process, to the autonomous vehicle equipped with the self-position estimation device 30 via communication means such as mobile communication, wireless LAN, or V2X. The environmental map data may be downloaded all at once for the area required by the autonomous vehicle. Alternatively, in order to reduce communication bandwidth, data for the area where the vehicle is expected to travel may be distributed on demand as needed, based on the vehicle's current position and direction of travel.

[0083] The autonomous vehicle uses the received environmental map and point cloud data obtained by the laser scanner to perform self-position estimation using the self-position estimation unit 34 of the self-position estimation device 30.

[0084] Data related to GNSS positioning suitability is used in autonomous vehicles (self-position estimation device 30) for the following purposes.

[0085] (i) This is used to determine the search range when an autonomous vehicle searches for an environmental map from its approximate position using the self-position estimation unit 34 of the self-position estimation device 30. In segments with high GNSS positioning suitability, the self-position estimation unit 34 is expected to have high accuracy in the approximate position output from the GNSS receiver 32 of the self-position estimation device 30, so it sets a narrower search range, and in segments with low GNSS positioning suitability, it sets a wider search range for the environmental map. This reduces the processing load on the self-position estimation unit 34 and reduces the risk of errors in the environmental map matching process due to insufficient accuracy of the approximate position.

[0086] (ii) When a composite positioning means such as GNSS / IMU is mounted on the autonomous vehicle's self-position estimation device 30 as a backup self-position estimation means in case the laser scanner 33 fails, the vehicle (self-position estimation device 30) can dynamically change the weighting of the positioning means based on the GNSS positioning suitability. The self-position estimation unit 34 increases the weighting of the GNSS positioning solution in the coupling process in segments with high GNSS positioning suitability. In segments with very high GNSS positioning suitability (for example, the top N segments (N is set in advance)), the cumulative error of the IMU can be corrected by the GNSS positioning solution. In segments with very low GNSS positioning suitability (for example, the bottom M segments (M is set in advance)), the GNSS positioning solution can be proactively separated from the composite positioning calculation and the system can switch to DR operation.

[0087] In addition, the GNSS positioning suitability can be used as alert information indicating the reliability of the self-position estimation result when the autonomous vehicle's self-position estimation device 30 outputs the self-position estimation result (coordinate values) to the control device.

[0088] (4) Map data update process The created environmental map is distributed through the (3) map data distribution process and used for self-position estimation by autonomous vehicles. The measurement vehicle (data collection device 20) continuously collects data from the target area periodically or irregularly, and the map production / distribution server 10 updates the environmental map data. In the data collection during the map data update process, the self-position estimation results from the environmental map are used, so it is expected that the accuracy of position estimation of the measurement vehicle will improve compared to the data collection during the initial production of the environmental map, and effective point cloud data will be collected. In the environmental map data update work, the difference between the new map data produced by the (2) map production process and the original map data is extracted from the measurement data, and the environmental map data is updated. Since the status of occluders (physical location) that affect GNSS positioning accuracy does not change frequently over time, the GNSS positioning suitability can basically be used continuously as a semi-static indicator. However, if the status of structures around the road changes, the data collected before the update is reset in each nearby segment, and the GNSS positioning suitability class classification is updated.

[0089] One of the roles of the map data update process, other than updating the environmental map data, is to improve the quality of the environmental map by increasing the number of effective GNSS positioning solutions in segments with low GNSS positioning suitability. In addition, as the cumulative amount of collected data increases, the granularity of segment division can be improved (the segment area can be narrowed). Furthermore, in segments with high GNSS positioning suitability, the quality of the environmental map can be checked by comparing the positioning results from the environmental map with the GNSS positioning solutions. In this way, by repeating the map data update process, it is possible not only to maintain the freshness of the environmental map data but also to continuously and gradually improve its quality.

[0090] Information on GNSS positioning suitability may be displayed, for example, as a heatmap on a map, so that it can be visually perceived by the autonomous vehicle's assistant driver or remote monitoring operator. This can serve as a trigger to alert the operator or prompt them to switch to manual driving. Furthermore, information on GNSS positioning suitability may be notified to the fleet management system via an API or similar means.

[0091] To improve the accuracy of the GNSS positioning suitability classification, data may be collected from general vehicles other than commercial vehicles using a crowdsourcing approach. In that case, the data acquisition device 20 may be equipped only with a GNSS receiver 22, a data storage unit 28, and a communication unit 29 instead of the configuration shown in Figure 2, and code positioning solutions may be collected using the lower-cost GNSS receiver 22.

[0092] This embodiment can be applied not only to autonomous driving but also to driver assistance systems such as ADAS (Advanced Driver-Assistance Systems) that use environmental maps.

[0093] As described above, according to this embodiment, by collecting a sufficient number of data at different time periods in the area where the environmental map is produced, and by statistical processing based on the positioning results to discard data with low effectiveness as GNSS positioning solutions, the efficiency of environmental map production and the quality of the environmental map can be improved.

[0094] Furthermore, by distributing information on the expected value of GNSS positioning accuracy along with environmental map data, the reliability of self-position estimation operations in autonomous driving can be improved.

[0095] In this embodiment, the map production and distribution server 10 is an example of an environmental map production device. The suitability determination unit 13 is an example of a calculation unit and a selection unit. The effectiveness testing unit 14 is an example of a selection unit.

[0096] Although embodiments of the present invention have been described in detail above, the present invention is not limited to these specific embodiments, and various modifications and changes are possible within the scope of the gist of the present invention as described in the claims. [Explanation of Symbols]

[0097] 10 Map creation and distribution server 11 Data receiving unit 12 Positioning calculation unit 13 Aptitude Judgment Department 14. Efficacy Testing Department 15. Mapmaking Department 16 Map Data Distribution Department 17 Data Storage Unit 20 Data Acquisition Devices 21 GNSS antenna 22 GNSS receivers 23 Laser Scanners 24 IMU 25 EDR 26 Odometry 27 Clock Department 28 Data Storage Section 29 Communications Department 30 Self-position estimation device 31 GNSS antenna 32 GNSS receivers 33 Laser Scanners 34 Self-position estimation part 35 Data Output Section 36 Data Storage Section 37 Communications Department 100 drive unit 101 Recording media 102 Auxiliary storage device 103 Memory device 104 Processors 105 Interface device B Bus

Claims

1. A calculation unit is configured to calculate an evaluation value for each of the multiple segments that divide the area, regarding whether the positioning solution based on data measured using GNSS by vehicles traveling in a certain area at multiple time periods is an effective solution close to the true value, A selection unit is configured to select the positioning solutions to be used in the production of an environmental map and the positioning solutions not to be used in the production of an environmental map based on the evaluation values. An environmental map production device characterized by having the following features.

2. The calculation unit is configured to calculate an evaluation value of the distribution state of the positioning solution as the evaluation value. The environmental map production apparatus according to claim 1, characterized in that it is a feature of the present invention.

3. The sorting unit is configured to sort a first segment into which all positioning solutions are used for creating an environmental map, and a second segment into which some of the positioning solutions are used for creating an environmental map. The environmental map production apparatus according to claim 1 or 2, characterized in that it is a feature of the present invention.

4. A selection unit is configured to select some of the positioning solutions related to the second segment based on a comparison between the height of the road surface at the position in the positioning solution and a reference value. The environmental map production apparatus according to claim 3, characterized by having the following features.

5. A selection unit is configured to select a portion of the positioning solutions related to the second segment based on a comparison between the location of a landmark determined based on the positioning solution and the true value of the landmark's location. The environmental map production apparatus according to claim 3, characterized by having the following features.

6. A selection unit is configured to select a positioning solution for the second segment in which the deviation between the positioning solution for the first segment and the estimated value of the vehicle's movement path is within a threshold. The environmental map production apparatus according to claim 3, characterized by having the following features.

7. A calculation procedure for calculating an evaluation value for each of the multiple segments that divide the area, regarding whether the positioning solution based on data measured using GNSS by vehicles traveling in a certain area at multiple time periods is an effective solution close to the true value, A selection procedure for selecting the positioning solutions to be used in the production of the environmental map and the positioning solutions not to be used in the production of the environmental map based on the evaluation values, A method for creating an environmental map, characterized in that a computer performs the following steps.

8. A calculation procedure for calculating an evaluation value for each of the multiple segments that divide the area, regarding whether the positioning solution based on data measured using GNSS by vehicles traveling in a certain area at multiple time periods is an effective solution close to the true value, A selection procedure for selecting the positioning solutions to be used in the production of the environmental map and the positioning solutions not to be used in the production of the environmental map based on the evaluation values, A program characterized by causing a computer to execute something.

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