Map creation system and automated driving system

Abstract

To provide a map creation system capable of easily creating wide-area maps at various heights. [Solution] The map creation system 10 includes a first three-dimensional point cloud data acquisition unit 11 that measures the surroundings and acquires three-dimensional point cloud data D1, and a wide-area map creation unit 12 that projects three-dimensional point cloud data D1a within a predetermined range Hr in the height direction from the three-dimensional point cloud data D1 acquired by the first three-dimensional point cloud data acquisition unit 11 onto a horizontal plane to create a two-dimensional wide-area map M1.

Description

[Technical field]

[0001] The present invention relates to a map creation system and an automated driving system. [Background technology]

[0002] Patent Document 1 discloses an invention of a mobile object control device that allows a mobile object having wheels to travel autonomously.

[0003] In the mobile object control device described in Patent Document 1, before the mobile object travels autonomously, the mobile object travels within a travel area to create an environment map (wide area map) based on SLAM (Simultaneous Localization and Mapping) technology. Then, the mobile object travels autonomously based on the environment map and the surrounding situation (two-dimensional point cloud data) recognized by a two-dimensional LiDAR (Light Detection And Ranging) mounted on the mobile object. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2022-13243 Summary of the Invention [Problem to be solved by the invention]

[0005] Two-dimensional LiDAR recognizes the surrounding situation in two dimensions (plane). The invention described in Patent Document 1 compares the surrounding situation recognized by the two-dimensional LiDAR with an environmental map, so it is necessary to prepare an environmental map in advance that matches the measurement position of the two-dimensional LiDAR.

[0006] Incidentally, in the invention described in Patent Document 1, there is a demand for multiple moving bodies to travel in the same space. In this case, if the installation positions (heights) of the two-dimensional LiDARs mounted on the moving bodies differ from one moving body to another, an environmental map (wide area map) that matches the installation positions (heights) of the two-dimensional LiDARs mounted on each moving body is required. In this case, it is necessary to create an environmental map (wide area map) that matches each installation position (height) before each moving body starts autonomous travel, which requires excessive effort and time.

[0007] An object of the present invention is to provide a map creation system that can easily create wide-area maps at various heights. [Means for solving the problem]

[0008] The present invention includes a first three-dimensional point cloud data acquisition unit that measures the surroundings and acquires three-dimensional point cloud data, and among the three-dimensional point cloud data acquired by the first three-dimensional point cloud data acquisition unit, On any horizontal plane 3D point cloud data within a specified range in the height direction In the height direction and a wide-area map creation unit that creates a two-dimensional wide-area map by projection. Effect of the Invention

[0009] According to the present invention, wide-area maps at various heights can be easily created. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram of a map creation system and an automatic driving system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a side view of a dolly in the map creation system according to the embodiment of the present invention. [Diagram 3] FIG. 3 is a top view of the dolly in the map creation system according to the embodiment of the present invention. [Figure 4] FIG. 4 is a diagram showing a given section R of a construction site. [Diagram 5] FIG. 5 is a side view of an automatic vehicle according to an embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart showing a flow for creating a wide-area map according to an embodiment of the present invention. [Figure 7] FIG. 7(A) is a diagram showing a portion of the data when three-dimensional point cloud data is extracted on one plane, and FIG. 7(B) is a diagram showing a portion of the data when three-dimensional point cloud data is extracted within a specified range and compressed. [Figure 8] FIG. 8 is a flowchart showing a flow when an automatic vehicle according to the embodiment of the present invention is caused to automatically travel. [Figure 9] FIG. 9 is a side view of an autonomous vehicle according to a modified example, and a diagram for explaining an extraction range of measured three-dimensional point cloud data. [Figure 10] FIG. 10 is a flowchart showing a flow for creating a wide-area map according to a modified example. [Figure 11] FIG. 11 is a flowchart showing a flow when an automatic vehicle according to a modified example is caused to automatically travel. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a map creation system 10 and an automated driving system 100 according to an embodiment of the present invention will be described with reference to the drawings.

[0012] The automatic driving system 100 of this embodiment is used to control automatic driving of an automatic driving body 1 used in, for example, a construction site, a logistics site, a production factory, a restaurant, or a medical site. The automatic driving system 100 includes a map creation system 10 and an automatic driving body 1. The automatic driving system 100 automatically drives the automatic driving body 1 using a wide-area map M1 created by the map creation system 10 and a surrounding map M2 created by a controller 20 mounted on the automatic driving body 1. In the following embodiment, a case where the map creation system 10 and the automatic driving system 100 are used in a construction site will be described.

[0013] First, the map creation system 10 will be described with reference to Figures 1 to 4. Figure 1 is a block diagram of an automatic driving system 100.

[0014] The map creation system 10 of this embodiment creates a wide-area map M1 required for automatically driving an automated vehicle 1 to a destination at a construction site.

[0015] As shown in Fig. 1, the map creation system 10 includes a first three-dimensional point cloud data acquisition unit 11 and a computer C. The computer C has a wide-area map creation unit 12, a memory unit 13, and a tilt correction unit 14. The first three-dimensional point cloud data acquisition unit 11 and the computer C are mounted on a dolly 15 (mobile body) (see Figs. 2 and 3). Note that the wide-area map creation unit 12 and the tilt correction unit 14 are virtual units representing the functions of the computer C, and do not represent physical entities.

[0016] In this embodiment, the wide-area map creation unit 12, the memory unit 13, and the tilt correction unit 14 are provided inside one computer C mounted on the dolly 15, but they do not have to be provided inside the same computer. In other words, the computer C is not limited to being configured by one computer, but may be configured by multiple computers. In addition, some or all of the wide-area map creation unit 12, the memory unit 13, and the tilt correction unit 14 may be provided in a computer installed in a location different from the dolly 15.

[0017] The first three-dimensional point cloud data acquisition unit 11 is configured by a three-dimensional LiDAR and is mounted on a dolly 15 (see FIG. 2). In the following, the three-dimensional LiDAR will be described with the same reference numeral "11" as the first three-dimensional point cloud data acquisition unit 11.

[0018] As shown in FIG. 2 and FIG. 3, the three-dimensional LiDAR 11 measures the periphery of the dolly 15 to acquire three-dimensional point cloud data. Specifically, the three-dimensional LiDAR 11 irradiates the target object with a laser light while scanning, and measures the reflected light to measure the distance and height to the target object (such as a wall or a pillar). The data acquired by the three-dimensional LiDAR 11 is a collection of three-dimensional points (hereinafter, the collection of data acquired by the three-dimensional LiDAR 11 is referred to as "three-dimensional point cloud data D1"). In this embodiment, the three-dimensional LiDAR 11 used has a measurement distance of several tens of meters to about 200 m. In addition, as shown in FIG. 2 and FIG. 3, the three-dimensional LiDAR 11 performs measurement within a range of a predetermined angle in the vertical direction and the horizontal direction with the three-dimensional LiDAR 11 as the center.

[0019] When creating a wide-area map M1 using the map creation system 10, for example, a worker moves thoroughly inside a section R in a construction site where an automated vehicle 1 is to automatically travel, pushing a cart 15 as shown in Fig. 4. During this time, the 3D LiDAR 11 constantly measures the periphery of the cart 15 and acquires 3D point cloud data D1.

[0020] The wide area map creation unit 12 creates a wide area map M1 based on three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11. In this embodiment, the wide area map M1 is created based on SLAM (Simultaneous Localization and Mapping) technology. Note that the wide area map M1 in this embodiment means a two-dimensional map of the entire section R including pillars, walls, obstacles, etc. A specific method for creating the wide area map M1 will be described in detail later.

[0021] The storage unit 13 stores the three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11, the wide-area map M1 created by the wide-area map creation unit 12, and the like. The storage unit 13 is configured by various storage devices such as a hard disk mounted on the computer C, or a storage medium such as a memory card or USB memory connectable to the computer C. The storage unit 13 may be mounted on the dolly 15, or may be provided in a computer or the like installed in a location separate from the dolly 15.

[0022] The tilt correction unit 14 extracts point cloud data of horizontal references (for example, a floor or ceiling) from the three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11, calculates the tilt of the extracted point cloud data, and corrects the tilt of the three-dimensional point cloud data D1 to be horizontal. Alternatively, a tilt angle sensor (not shown) may be provided on the dolly 15, and the tilt angle and tilt direction of the dolly 15 may be detected by the tilt angle sensor, and the tilt of the measured three-dimensional point cloud data D1 may be corrected based on the detected tilt angle and tilt direction.

[0023] Next, the automatic vehicle 1 will be described.

[0024] The autonomous vehicle 1 of this embodiment is, for example, an autonomous transport vehicle. As shown in Fig. 5, the autonomous vehicle 1 includes a vehicle body 2, four wheels 3 arranged on the front, rear, left and right sides of the vehicle body 2, a battery 4 that supplies power to a motor (not shown) that drives the wheels 3, a two-dimensional point cloud data acquisition unit 5, and a controller 20 that controls the travel of the autonomous vehicle 1. A loading platform 6 on which luggage is placed is installed on the vehicle body 2. Note that the autonomous vehicle 1 does not necessarily have to be an autonomous vehicle having a transport function.

[0025] 1, the controller 20 includes a surrounding area map creation unit 21, a storage unit 22, a control unit 23, and a transmission / reception unit 24. Note that the surrounding area map creation unit 21 and the control unit 23 are virtual units that represent the functions of the controller 20, and do not represent physical entities.

[0026] In this embodiment, the two-dimensional point cloud data acquisition unit 5 is configured by a two-dimensional LiDAR and is mounted on the autonomous vehicle 1. In the following, the two-dimensional LiDAR mounted on the autonomous vehicle 1 will be described with the same reference number "5" as the two-dimensional point cloud data acquisition unit 5. The two-dimensional LiDAR 5 measures the surroundings of the autonomous vehicle 1 and acquires two-dimensional point cloud data D2.

[0027] The surrounding area map creation unit 21 creates a map of the surroundings of the autonomous vehicle 1 (hereinafter, referred to as a "surrounding area map M2") based on the two-dimensional point cloud data D2 acquired by the two-dimensional LiDAR 5. A specific method for creating the surrounding area map M2 will be described in detail later.

[0028] The storage unit 22 is configured with a readable / writable storage medium such as a hard disk or SSD. The storage unit 22 stores two-dimensional point cloud data D2 measured by the two-dimensional LiDAR 5 and a wide-area map M1 created by the map creation system 10, as well as various programs for controlling the traveling operation of the automated vehicle 1. Furthermore, the storage unit 22 stores various data such as the destination of the automated vehicle 1 and the route to the destination.

[0029] The storage unit 22 may be configured by a storage medium (for example, a memory card) that is detachable from the controller 20. Alternatively, the storage unit 22 may be configured by a storage medium installed in a location different from that of the controller 20. Furthermore, these may be used in combination.

[0030] The control unit 23 controls the traveling operation of the automated vehicle 1 based on the wide-area map M1 stored in the memory unit 22 and the surrounding area map M2 created by the surrounding area map creation unit 21. Specifically, when a destination is input to the automated vehicle 1, the control unit 23 calculates a route to the destination while taking into consideration obstacle avoidance and the like. Thereafter, the control unit 23 drives the wheels 3 by a motor (not shown) to automatically travel the automated vehicle 1 to the destination while estimating the current position of the automated vehicle 1 based on the surrounding area map M2 created at any time while the automated vehicle 1 is traveling and the wide-area map M1 stored in the memory unit 22.

[0031] The transmission / reception unit 24 performs data communication between the controller 20 and an external computer. Through the transmission / reception unit 24, the controller 20 can transmit, for example, information such as the current position information of the automated vehicle 1 and the driving route to the external computer, and can receive a wide-area map M1, destination information, and the like from the external computer.

[0032] In this way, the autonomous driving system 100 can automatically drive the autonomous vehicle 1 to a destination based on the wide-area map M1 created by the map creation system 10 and the surrounding map M2 created based on the two-dimensional point cloud data D2 acquired by the two-dimensional LiDAR 5 of the autonomous vehicle 1.

[0033] Incidentally, the two-dimensional point cloud data D2 acquired by the two-dimensional LiDAR 5 of the automatic vehicle 1 is two-dimensional (on a horizontal plane) point cloud data. In contrast, the three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11 in the map creation system 10 is three-dimensional (solid) point cloud data. However, the three-dimensional point cloud data D1 is a collection of sparse point data. For this reason, for example, if only plane data at the same height as the two-dimensional point cloud data D2 is extracted from the three-dimensional point cloud data D1 in order to compare it with the two-dimensional point cloud data D2, the map will be composed of sparse points, as shown in FIG. 7(A), in other words, it will be a map with a small amount of information (low accuracy). If the automatic vehicle 1 is automatically driven using such a map, for example, there is a risk that the self-position of the automatic vehicle 1 cannot be estimated.

[0034] Furthermore, when using the automatic driving system 100, multiple automatic vehicles 1 may be driven within the section R. In this case, if the installation position (height H in FIG. 5) of the two-dimensional LiDAR 5 mounted on each automatic driving body 1 differs depending on the automatic driving body 1, a wide-area map M1 that matches the installation position (height H) of the two-dimensional LiDAR mounted on each automatic driving body 1 is required. For this reason, before each automatic driving body 1 is driven automatically, it becomes necessary to create a wide-area map M1 that matches the installation position (height H) of each two-dimensional LiDAR 5, which requires a lot of labor and time.

[0035] Therefore, the map creation system 10 of this embodiment employs a method for creating a wide-area map M1 that can easily correspond to the installation position (height) of the two-dimensional LiDAR 5 and has high accuracy, by using three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11. A specific method for creating the wide-area map M1 will be described below with reference to the flowchart shown in FIG.

[0036] In step S11, three-dimensional point cloud data D1 is acquired. Specifically, while the dolly 15 is moving, the three-dimensional LiDAR 11 measures the surroundings of the dolly 15 to acquire the three-dimensional point cloud data D1.

[0037] In step S12, tilt correction is performed. Specifically, point cloud data of a horizontal reference (for example, a floor surface or a ceiling) is extracted from the three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11, the tilt of the extracted point cloud data is calculated, and the tilt of the three-dimensional point cloud data D1 is corrected to be horizontal. Note that if tilt correction is not required, the process of step S13 may be omitted.

[0038] In step S13, the height (height H) of the installation position of the two-dimensional LiDAR 5 mounted on the autonomous vehicle 1 is input. Specifically, the height H from the floor surface of the two-dimensional point cloud data D2 measured by the two-dimensional LiDAR 5 mounted on the autonomous vehicle 1 is input to the computer C.

[0039] In step S14, three-dimensional point cloud data D1a of a predetermined range Hr is extracted. Specifically, the computer C (wide area map creation unit 12) extracts three-dimensional point cloud data D1a within a predetermined range Hr in the height direction from the three-dimensional point cloud data D1 acquired by the three-dimensional LiDAR 11. In this embodiment, the range Hr is set to a range of, for example, about ±20 cm centered on the height H of a plane (the installation position of the two-dimensional LiDAR 5) measured by the two-dimensional LiDAR 5 mounted on the automatic vehicle 1 (see FIG. 2).

[0040] In step S15, the three-dimensional point cloud data D1a is compressed. Specifically, the computer C (wide area map creation unit 12) projects the three-dimensional point cloud data D1a within the range Hr onto a horizontal plane to create a wide area map M1. In other words, the point cloud data within the range Hr is superimposed on one plane to create a two-dimensional wide area map M1. This reduces the gap between data (see FIG. 7(B)) compared to the case where only data on one plane of the three-dimensional point cloud data D1 is extracted (see FIG. 7(A)). As a result, the data density increases, making it possible to create a wide area map M1 with high accuracy.

[0041] In addition, when autonomous vehicles 1 with different heights H of the installation position of the two-dimensional LiDAR 5 are operated autonomously, a wide-area map M1 corresponding to the autonomous vehicle 1 can be easily created by simply changing the input of the height H of the installation position of the two-dimensional LiDAR 5 in step S12 above according to the height of the installation position of the two-dimensional LiDAR 5 of each autonomous vehicle 1.

[0042] Next, the travel control of the autonomous vehicle 1 in the autonomous driving system 100 will be described with reference to the flowchart shown in Fig. 8. The process shown in the flowchart in Fig. 8 is executed based on a program prestored in the storage unit 22 of the controller 20. The controller 20 repeats the process shown in Fig. 8 several times per second.

[0043] The process shown in FIG. 8 is a process that is performed while the automatic vehicle 1 is automatically traveling after a route to the destination of the automatic vehicle 1 is set in the controller 20.

[0044] In step S21, two-dimensional point cloud data D2 is acquired. Specifically, the two-dimensional point cloud data D2 of the surroundings of the autonomous vehicle 1 is acquired by the two-dimensional LiDAR 5 mounted on the autonomous vehicle 1.

[0045] In step S22, the self-position is estimated. Specifically, the controller 20 (periphery map creation unit 21) creates a peripheral map M2 of the surroundings of the automated vehicle 1 from the two-dimensional point cloud data D2 acquired by the two-dimensional LiDAR 5. Next, the controller 20 (control unit 23) compares the wide-area map M1 stored in advance in the storage unit 22 with the created peripheral map M2 to estimate the current position (self-position) of the automated vehicle 1.

[0046] In step S23, the presence or absence of an obstacle is determined. Specifically, the controller 20 (control unit 23) determines whether or not an obstacle has appeared on the route to the set destination based on the surrounding map M2 created in step S22 or the two-dimensional point cloud data D2. For example, at a construction site, materials or equipment that did not exist at the stage when the wide-area map M1 was created may be placed on the set route at the stage when the automatic vehicle 1 travels. The controller 20 (control unit 23) detects an obstacle that has appeared in this way at the stage when the automatic vehicle 1 travels. If an obstacle is not detected on the route to the set destination, the process proceeds to step S24, and if an obstacle is detected on the route to the set destination, the process proceeds to step S25. The detection of an obstacle may be performed by comparing the wide-area map M1 with the surrounding map M2.

[0047] In step S24, it is determined whether the destination has been reached. Specifically, the controller 20 (control unit 23) determines whether the current position of the automatic vehicle 1 estimated in step S22 is the destination. If the automatic vehicle 1 has reached the destination, the control (automatic driving) is terminated. On the other hand, if the automatic vehicle 1 has not reached the destination, the process returns to step S21.

[0048] Next, step S25 will be described. As described above, if it is determined in step S23 that an obstacle exists on the route to the set destination, the process proceeds to step S25. In step S25, the route is reset. Specifically, if the controller 20 (control unit 23) detects an obstacle on the set route, the controller 20 searches for a route that allows the automated vehicle 1 to avoid the obstacle and reach the destination, and resets the route. Then, the process returns to step S21, and the process from step S21 onward is executed again.

[0049] As described above, in the map creation system 10 of this embodiment, the three-dimensional point cloud data D1a within a predetermined height range Hr is projected onto a horizontal plane to create the wide-area map M1, so that a highly accurate wide-area map M1 can be created. In addition, in the automatic driving system 100 of this embodiment, the highly accurate wide-area map M1 is used when the automatic driving body 1 is caused to automatically drive, so that the estimation accuracy of the self-position of the automatic driving body 1 can be improved, and the accuracy of the automatic driving can also be improved.

[0050] Furthermore, in the map creation system 10, by simply changing the input of the height H of the installation position of the two-dimensional LiDAR 5, it is possible to easily create a wide-area map M1 according to the height H of the installation position of the two-dimensional LiDAR 5 of the autonomous vehicle 1. In other words, by using the map creation system 10, it is possible to easily create wide-area maps M1 at various heights, and therefore it is possible to significantly reduce the effort and time required to recreate the wide-area map M1 when using an autonomous vehicle 1 with a different installation position of the two-dimensional LiDAR 5.

[0051] Next, a modified example of the automatic driving system 100 will be described. In the automatic driving system 100, the automatic vehicle 1 is equipped with a two-dimensional LiDAR 5, but in the automatic driving system 100 according to the modified example, a three-dimensional LiDAR 50 (a second three-dimensional point cloud data acquisition unit) is equipped on the automatic vehicle 1 (see FIG. 9), which is different from the modified example. In addition, in the automatic driving system 100, the wide-area map M1 is created according to the height H of the installation position of the two-dimensional LiDAR 5, but in the automatic driving system 100 according to the modified example, the wide-area map M11 can be created at any height, which is different from the modified example. In the following, the same configurations and the same processes are denoted by the same numbers and the description is omitted as appropriate.

[0052] First, a method for creating the wide area map M11 in the modified example will be described with reference to the flowchart shown in Fig. 10. Note that only steps S113, S114, and S115 that differ from the flowchart shown in Fig. 6 will be described here.

[0053] In this modification, three-dimensional point cloud data D1 measured by the three-dimensional LiDAR 11 mounted on the cart 15 is also used. In step S113, height H1 (see FIG. 9) is input. Specifically, height H1 from the floor surface is input to computer C. Height H1 is an arbitrary height and does not need to correspond to the installation position of the three-dimensional LiDAR 50 mounted on the autonomous vehicle 1. In this modification, height H1 is set to about 2 m, for example (see FIG. 9).

[0054] Then, in step S114, three-dimensional point cloud data D1b of a predetermined range Hr is extracted. Specifically, the computer C (wide area map creation unit 12) extracts three-dimensional point cloud data D1b within a predetermined range Hr1 in the height direction from the three-dimensional point cloud data D1 acquired by the three-dimensional LiDAR 11. In this modification, the predetermined range Hr1 from which the three-dimensional point cloud data D1 is extracted is, for example, a range of about height H1±50 cm (see FIG. 9).

[0055] At construction sites, materials and equipment are sometimes temporarily placed on the floor (see FIG. 9). If a wide-area map M11 is created in this state, the materials and equipment will naturally be reflected in the wide-area map M11. However, when the autonomous vehicle 1 is made to travel automatically, the materials and equipment placed on the floor may have moved. In this case, there may be a difference between the wide-area map M11 created in advance and the surrounding area map M2 acquired by the autonomous vehicle 1 during automatic travel, and the controller 20 (control unit 23) may not be able to estimate the current position of the autonomous vehicle 1.

[0056] Therefore, by setting the height H1 of the extraction range of the three-dimensional point cloud data D1 to about 2 m (the range Hr1 is from about 1.5 m to 2.5 m in height) as in this modified example, the wide-area map M11 can be created using point cloud data of a range that does not include materials and equipment temporarily placed on the floor. In other words, the wide-area map M11 can be created without being affected by materials and equipment.

[0057] In step S115, the three-dimensional point group data D1b is compressed. The specific method is the same as that in step S15, and therefore the description will be omitted.

[0058] Next, the travel control of the automated vehicle 1 in this modified example will be described with reference to the flowchart shown in Fig. 11. The process shown in the flowchart in Fig. 11 is executed based on a program prestored in the storage unit 22 of the controller 20. The controller 20 repeats the process shown in Fig. 11 several times per second.

[0059] In this modified example, the surrounding map M12 created based on the three-dimensional point cloud data D3 measured by the three-dimensional LiDAR 50 mounted on the autonomous vehicle 1 is created in the same manner as the wide-area map M11 (flowchart in FIG. 10). Although not shown, to briefly explain, the predetermined range Hr1 in the height direction of the three-dimensional point cloud data D3 acquired by the three-dimensional LiDAR 50 is made the same as the extraction range (height H1±50 cm or so) of the three-dimensional point cloud data D1. Note that when creating the surrounding map M12, once the height H1 is input in step S112 in FIG. 10, thereafter, it is not necessary to input the height H1 (step S112).

[0060] In this way, by creating the wide-area map M11 and the surrounding map M12 under the same conditions, the estimation accuracy of the self-position can be improved. Therefore, optimally, it is desirable that the 3D LiDAR 50 mounted on the automatic vehicle 1 is the same model as the 3D LiDAR 11 mounted on the bogie 15.

[0061] Also, in the automated driving system 100 of this modified example, the extraction range of the data of the three-dimensional point cloud data D1, D3, i.e., the height of the wide-area map M11 and the surrounding area map M12 to be created, can be changed as appropriate. For example, by changing the extraction range (height H, H1) of the data of the three-dimensional point cloud data D1, D3 according to the height of materials and equipment placed on the floor surface and the progress of construction, it is possible to create the wide-area map M11 and the surrounding area map M12 of a range that is not affected by these as appropriate. This can improve the estimation accuracy of the self-position of the automated driving body 1, and can also improve the accuracy of automated driving.

[0062] The above-described map creation system 10 and automatic driving system 100 provide the following advantages.

[0063] In the map creation system 10, three-dimensional point cloud data D1a within a predetermined range Hr in the height direction is extracted from the three-dimensional point cloud data D1 acquired by the three-dimensional LiDAR 11, and the three-dimensional point cloud data D1a is compressed to create a two-dimensional wide-area map M1. This makes it possible to create a highly accurate two-dimensional wide-area map M1 even when the three-dimensional point cloud data D1 is used.

[0064] Furthermore, in the map creation system 10, two-dimensional wide area maps M1 of different heights can be easily created simply by appropriately changing the range Hr (input of the height H). In other words, by using the map creation system 10, two-dimensional wide area maps M1 of various heights can be easily created, so that, for example, when using multiple automated vehicles 1 with different installation positions of the two-dimensional LiDAR 5, the effort and time required to create the wide area maps M1 corresponding to each automated vehicle 1 can be significantly reduced.

[0065] Furthermore, in the autonomous driving system 100 of this embodiment, when the autonomous vehicle 1 is driven autonomously, the high-precision wide-area map M1 created by the map creation system 10 is used, thereby improving the estimation accuracy of the autonomous vehicle 1's own position and thereby improving the accuracy of autonomous driving.

[0066] Furthermore, when the automated vehicle 1 is equipped with a three-dimensional LiDAR 50, the data extraction range (height H, H1) of the three-dimensional point cloud data D1, D3 can be changed according to the height of materials and equipment placed on the floor surface and the progress of the construction work, so that the wide-area map M11 and the surrounding area map M12 of the range that are not affected by these can be appropriately created. This can further improve the estimation accuracy of the self-position of the automated vehicle 1, and can also further improve the accuracy of automated driving.

[0067] Although the embodiments of the present invention have been described above, the above-mentioned embodiments merely show some of the application examples of the present invention, and it is not intended that the technical scope of the present invention be limited to the specific configurations of the above-mentioned embodiments.

[0068] In the above embodiment, the three-dimensional LiDAR 11, 50 is used to acquire three-dimensional point cloud data, but the method of acquiring three-dimensional point cloud data is not limited to this. For example, the three-dimensional point cloud data may be acquired from VisualSLAM technology using an imaging device such as a camera, or the three-dimensional point cloud data may be acquired by moving a two-dimensional LiDAR three-dimensionally.

[0069] In the above embodiment, the surrounding area map M2 is created from the two-dimensional point cloud data D2 and the process is then carried out. However, the two-dimensional point cloud data D2 may be used as is.

[0070] In the above embodiment, the three-dimensional point cloud data D1 is acquired by the three-dimensional LiDAR 11 mounted on the dolly 15 pushed by the worker, but the present invention is not limited to this. For example, the three-dimensional LiDAR 11 may be mounted on a remote-controllable dolly, and the dolly may be remotely operated to acquire the three-dimensional point cloud data D1. In addition, the worker may move while holding the three-dimensional LiDAR 11 in his / her hand, or may acquire the three-dimensional point cloud data D1 by mounting the three-dimensional LiDAR 11 on a helmet or wearing it on his / her arm and walking within the section R.

[0071] Furthermore, in the above embodiment, the three-dimensional point cloud data D1 is acquired while moving the three-dimensional LiDAR 11, but the three-dimensional point cloud data D1 does not necessarily have to be acquired while moving the three-dimensional LiDAR 11. For example, the three-dimensional point cloud data D1 acquired at multiple points may be superimposed, and the wide-area map M1 may be created based on the superimposed three-dimensional point cloud data D1. Also, the three-dimensional point cloud data may be acquired using images captured at fixed points by an imaging device such as a camera. [Explanation of symbols]

[0072] 100 Autonomous Driving System 10. Map Creation System 1 Autonomous vehicle 5 2D LiDAR (2D point cloud data acquisition unit) 11 3D LiDAR (first 3D point cloud data acquisition unit) 12 Wide Area Map Creation Department 13 Storage section 14 Tilt correction section 15 Cart (mobile) 20 Controller 21 Surrounding Area Map Creation Department 22 Memory section 23 Control Unit 50 3D LiDAR (2nd 3D point cloud data acquisition unit) C Computer

Claims

1. a first three-dimensional point cloud data acquisition unit that measures the surroundings and acquires three-dimensional point cloud data; a wide-area map creation unit that projects three-dimensional point cloud data within a predetermined range in the height direction onto any horizontal plane, from the three-dimensional point cloud data acquired by the first three-dimensional point cloud data acquisition unit, in the height direction to create a two-dimensional wide-area map.

2. An autonomous vehicle that automatically travels to a destination; A storage unit that stores the wide-area map obtained by the map creation system according to claim 1; a two-dimensional point cloud data acquisition unit mounted on the automated driving body and configured to measure a periphery of the automated driving body and acquire two-dimensional point cloud data on a predetermined horizontal plane; a control unit that controls a traveling operation of the automated driving vehicle based on the wide-area map stored in the storage unit and the two-dimensional point cloud data acquired by the two-dimensional point cloud data acquisition unit, The wide-area map creation unit is an automated driving system that creates the wide-area map from three-dimensional point cloud data within a predetermined range including the horizontal plane measured by the two-dimensional point cloud data acquisition unit.

3. An autonomous vehicle that automatically travels to a destination; A storage unit that stores the wide-area map obtained by the map creation system according to claim 1; A second three-dimensional point cloud data acquisition unit that is mounted on the automated driving body and measures the surroundings of the automated driving body to acquire three-dimensional point cloud data; a surroundings map creation unit that creates a two-dimensional surroundings map of the surroundings of the automated driving vehicle by projecting, in a height direction, three-dimensional point cloud data within a predetermined range in the height direction, among the three-dimensional point cloud data acquired by the second three-dimensional point cloud data acquisition unit, onto any one plane in the horizontal direction; An autonomous driving system comprising: a control unit that controls the driving operation of the autonomous driving body based on the wide-area map stored in the memory unit and the surrounding area map created by the surrounding area map creation unit.

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