Map creation system and automatic traveling system

Abstract

To provide a map creation system that can easily create wide-area maps with various heights.SOLUTION: A map creation system 10 comprises: a first three-dimensional point group data acquisition unit 11 that measures the surroundings of an automatic traveling body to acquire three-dimensional point cloud data D1; and a wide-area map creation unit 12 that creates a two-dimensional wide-area map M1 by projecting three-dimensional point cloud data D1a within a predetermined range Hr in a height direction, of the three-dimensional point cloud data D1 acquired by the first three-dimensional point cloud data acquisition unit 11, on a plane in a horizontal direction.SELECTED DRAWING: Figure 1

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 body control device that allows a mobile body having wheels to travel autonomously.

[0003] In the mobile body control device described in Patent Document 1, before the mobile body starts autonomous driving, an environmental map (wide area map) is created based on SLAM (Simultaneous Localization and Mapping) technology by driving the mobile body within a driving area.Then, the mobile body drives autonomously based on the environmental map and the surrounding situation (two-dimensional point cloud data) recognized by a two-dimensional LiDAR (Light Detection And Ranging) mounted on the mobile body. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese 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 2D LiDARs mounted on the moving bodies differ depending on the moving body, an environmental map (wide-area map) tailored to the installation positions (heights) of the 2D LiDARs mounted on each moving body is required. In this case, an environmental map (wide-area map) tailored to each installation position (height) must be created before each moving body can travel autonomously, 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 comprises a first three-dimensional point cloud data acquisition unit that measures the surroundings and acquires three-dimensional point cloud data, and a wide-area map creation unit that projects three-dimensional point cloud data within a predetermined range in the height direction, from the three-dimensional point cloud data acquired by the first three-dimensional point cloud data acquisition unit, onto a horizontal plane to create a two-dimensional wide-area map. [Effects of the Invention]

[0009] According to the present invention, wide-area maps at various heights can be easily created. [Brief explanation 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. [Figure 2] FIG. 2 is a side view of the cart in the map creation system according to the embodiment of the present invention. [Figure 3] FIG. 3 is a top view of the cart in the map creation system according to the embodiment of the present invention. [Figure 4] FIG. 4 is a diagram showing a predetermined section R of a construction site. [Figure 5]FIG. 5 is a side view of an autonomous 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] Figure 7(A) is a diagram showing a portion of the data when three-dimensional point cloud data is extracted on one plane, and Figure 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 autonomous vehicle is caused to travel automatically according to an embodiment of the present invention. [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 autonomous vehicle according to a modified example is caused to autonomously travel. DETAILED DESCRIPTION OF THE INVENTION

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

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

[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 an automated vehicle 1 to automatically travel to a destination at a construction site.

[0015] As shown in FIG. 1, a map creation system 10 includes a first three-dimensional point cloud data acquisition unit 11 and a computer C. The computer C includes 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 cart 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 that represent 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 cart 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. Furthermore, 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 cart 15.

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

[0018] As shown in FIGS. 2 and 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 measures the distance and height to the object (such as a wall or pillar) by scanning and irradiating the object with laser light and measuring the reflected light. 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"). The three-dimensional LiDAR 11 used in this embodiment has a measurement distance of approximately several tens of meters to 200 meters. As shown in FIGS. 2 and 3, the three-dimensional LiDAR 11 performs measurements within a predetermined angular range in the vertical and horizontal directions around the three-dimensional LiDAR 11.

[0019] When creating a wide-area map M1 using the map creation system 10, for example, a worker pushes a cart 15 throughout a section R of a construction site where an autonomous vehicle 1 is to automatically travel, 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 refers to 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 storage media such as a memory card or USB memory connectable to the computer C. The storage unit 13 may be mounted on the cart 15, or may be provided in a computer or the like installed in a location separate from the cart 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 so that it is horizontal. Alternatively, an inclination angle sensor (not shown) may be provided on the dolly 15, and the inclination angle and tilt direction of the dolly 15 may be detected by this inclination angle sensor, and the tilt of the measured three-dimensional point cloud data D1 may be corrected based on the detected inclination 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 traveling of the autonomous vehicle 1. A loading platform 6 on which luggage can be placed is installed on the vehicle body 2. Note that the autonomous vehicle 1 does not necessarily have to be an autonomous vehicle with 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 by assigning the same reference numeral "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 "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 storage medium such as a readable / writable 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 autonomous vehicle 1. The storage unit 22 also stores various data such as the destination of the autonomous vehicle 1 and the route to the destination.

[0029] The storage unit 22 may be configured as a storage medium (for example, a memory card) that is detachable from the controller 20. Alternatively, the storage unit 22 may be configured as a storage medium that is installed in a location different from 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, etc. Thereafter, the control unit 23 estimates the current position of the automated vehicle 1 based on the surrounding area map M2, which is created as needed while the automated vehicle 1 is traveling, and the wide-area map M1 stored in the memory unit 22, and drives the wheels 3 with a motor (not shown) to automatically travel the automated vehicle 1 to the destination.

[0031] The transmitter / receiver 24 performs data communication between the controller 20 and an external computer. Through the transmitter / receiver 24, the controller 20 can transmit, for example, information such as the current location information of the autonomous vehicle 1 and the driving route to the external computer, and can also receive information such as a wide-area map M1 and destination information from the external computer.

[0032] In this way, the automatic driving system 100 can automatically drive the automatic driving vehicle 1 to the 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 automatic driving vehicle 1.

[0033] The two-dimensional point cloud data D2 acquired by the two-dimensional LiDAR 5 of the autonomous 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 resulting map will be composed of sparse points, as shown in FIG. 7(A), in other words, a map with little information (low accuracy). If the autonomous vehicle 1 is driven automatically using such a map, for example, there is a risk that the autonomous vehicle 1 will not be able to estimate its own position.

[0034] Furthermore, when using the automated driving system 100, multiple automated 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 automated vehicle 1 differs depending on the automated vehicle 1, a wide-area map M1 tailored to the installation position (height H) of the two-dimensional LiDAR mounted on each automated vehicle 1 is required. For this reason, before each automated vehicle 1 is driven automatically, it becomes necessary to create a wide-area map M1 tailored to the installation position (height H) of each two-dimensional LiDAR 5, which requires a lot of effort and time.

[0035] Therefore, the map creation system 10 of this embodiment employs a method for creating a highly accurate wide-area map M1 that can easily correspond to the installation position (height) of the two-dimensional LiDAR 5, 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 or 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 processing 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 within 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 around the height H of the plane measured by the two-dimensional LiDAR 5 mounted on the autonomous vehicle 1 (the installation position of the two-dimensional LiDAR 5) (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 overlaid on a single plane to create a two-dimensional wide-area map M1. This reduces the gaps between data (see FIG. 7(B)) compared to when only data from 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 highly accurate wide-area map M1.

[0041] Furthermore, when autonomous vehicles 1 with different installation heights H of the two-dimensional LiDAR 5 are operated automatically, a wide-area map M1 corresponding to the autonomous vehicle 1 can be easily created simply by changing the input of the installation height H of the two-dimensional LiDAR 5 in step S12 above according to the installation height 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 processing shown in the flowchart in Fig. 8 is executed based on a program stored in advance in the storage unit 22 of the controller 20. The controller 20 repeats the processing shown in Fig. 8 several times per second.

[0043] The processing shown in FIG. 8 is processing that is performed while the autonomous vehicle 1 is traveling automatically, with the route to the destination of the autonomous vehicle 1 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 around 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 autonomous 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, and estimates the current position (self-position) of the autonomous 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 area 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 when the wide-area map M1 was created may be placed on the set route when the automated vehicle 1 is traveling. The controller 20 (control unit 23) detects obstacles that have appeared in this way when the automated vehicle 1 is traveling. If an obstacle is not detected on the route to the set destination, the process proceeds to step S24. If an obstacle is detected on the route to the set destination, the process proceeds to step S25. Note that obstacle detection may be performed by comparing the wide-area map M1 with the surrounding area 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 reach the destination while avoiding the obstacle, and resets the route. Then, the process returns to step S21, and the processes from step S21 onwards are 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, making it possible to create a highly accurate wide-area map M1. Furthermore, in the automated driving system 100 of this embodiment, the highly accurate wide-area map M1 is used when the automated driving vehicle 1 is driven automatically, making it possible to improve the accuracy of estimating the self-position of the automated driving vehicle 1 and also improve the accuracy of automated driving.

[0050] Furthermore, with the map creation system 10, simply by 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 that corresponds 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, which significantly reduces 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 automated driving system 100 will be described. In the automated driving system 100, a two-dimensional LiDAR 5 is mounted on the automated vehicle 1, but in the automated driving system 100 according to the modified example, a three-dimensional LiDAR 50 (second three-dimensional point cloud data acquisition unit) is mounted on the automated vehicle 1 (see FIG. 9 ), which is different. Also, in the automated driving system 100, the wide-area map M1 is created in accordance with the height H of the installation position of the two-dimensional LiDAR 5, but in the automated driving system 100 according to the modified example, a wide-area map M11 can be created at any height, which is different. Note that, below, the same configurations and the same processes are denoted with the same numbers, and descriptions thereof will be 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 carriage 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, for example, 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 driven automatically, the materials and equipment that were placed on the floor may have moved. In this case, there may be a discrepancy between the wide-area map M11 created in advance and the surrounding area map M2 acquired by the autonomous vehicle 1 during automatic driving, which may prevent the controller 20 (control unit 23) from estimating 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 about 1.5 m to 2.5 m in height) as in this modification, it is possible to create the wide-area map M11 using point cloud data in a range that does not include materials or equipment temporarily placed on the floor. In other words, it is possible to create the wide-area map M11 without being affected by materials or 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, so the explanation 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 processing shown in the flowchart in Fig. 11 is executed based on a program pre-stored in the storage unit 22 of the controller 20. The controller 20 repeats the processing shown in Fig. 11 several times per second.

[0059] In this modified example, the surroundings map M12 is created based on the three-dimensional point cloud data D3 measured by the three-dimensional LiDAR 50 mounted on the autonomous vehicle 1, and is created in the same manner as the wide-area map M11 (flowchart in FIG. 10). Although not shown, to briefly explain, a predetermined range Hr1 in the height direction of the three-dimensional point cloud data D3 acquired by the three-dimensional LiDAR 50 is set to be the same as the extraction range of the three-dimensional point cloud data D1 (height H1 ± 50 cm). Note that when creating the surroundings map M12, once the height H1 is input in step S112 in FIG. 10, there is no need to input the height H1 again (step S112).

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

[0061] Furthermore, 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 needed. For example, by changing the extraction range (heights H, H1) of the data of the three-dimensional point cloud data D1, D3 depending on the height of materials and equipment placed on the floor and the progress of construction, it is possible to create the wide-area map M11 and the surrounding area map M12 of ranges that are not affected by these factors. This improves the accuracy of estimating the self-position of the automated driving vehicle 1, and also improves the accuracy of automated driving.

[0062] The map creation system 10 and the automatic driving system 100 described above have 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 three-dimensional point cloud data D1 acquired by a three-dimensional LiDAR 11, and this 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 using three-dimensional point cloud data D1.

[0064] Furthermore, with 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 two-dimensional LiDARs 5 installed in different positions, the effort and time required to create wide-area maps M1 corresponding to each automated vehicle 1 can be significantly reduced.

[0065] Furthermore, in the automatic driving system 100 of this embodiment, when the automatic driving vehicle 1 is driven automatically, the high-precision wide-area map M1 created by the map creation system 10 is used, thereby improving the accuracy of estimating the self-position of the automatic driving vehicle 1 and also improving the accuracy of automatic driving.

[0066] Furthermore, when the automated vehicle 1 is equipped with a 3D LiDAR 50, the data extraction range (heights H, H1) of the 3D point cloud data D1, D3 can be changed according to the height of materials and equipment placed on the floor and the progress of construction, making it possible to appropriately create a wide-area map M11 and a surrounding area map M12 of an area that is not affected by these factors. This can further improve the accuracy of estimating the self-position of the automated vehicle 1, and also further improve the accuracy of automated driving.

[0067] Although the embodiments of the present invention have been described above, the above embodiments merely illustrate 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 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, three-dimensional point cloud data may be acquired using VisualSLAM technology using an imaging device such as a camera, or three-dimensional point cloud data may be acquired by moving a two-dimensional LiDAR three-dimensionally.

[0069] Furthermore, in the above embodiment, the surrounding area map M2 is created from the two-dimensional point cloud data D2 and the processing is then carried out, but 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 cart 15 pushed by a worker. However, this is not limiting. For example, the three-dimensional LiDAR 11 may be mounted on a remotely controllable cart, and the cart may be remotely operated to acquire the three-dimensional point cloud data D1. Alternatively, the worker may carry the three-dimensional LiDAR 11 in his / her hand while moving, or may acquire the three-dimensional point cloud data D1 by walking within the section R with the three-dimensional LiDAR 11 mounted on a helmet or attached to his / her arm.

[0071] Furthermore, in the above embodiment, the three-dimensional point cloud data D1 is acquired while the three-dimensional LiDAR 11 is moved. However, the three-dimensional point cloud data D1 does not necessarily have to be acquired while the three-dimensional LiDAR 11 is moved. For example, three-dimensional point cloud data D1 acquired at multiple locations may be superimposed, and the wide-area map M1 may be created based on this superimposed three-dimensional point cloud data D1. Alternatively, the three-dimensional point cloud data may be acquired using images captured at fixed locations 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, out of the three-dimensional point cloud data acquired by the first three-dimensional point cloud data acquisition unit, onto a horizontal plane to create a two-dimensional wide-area map.

2. An autonomous vehicle that automatically travels to its 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 vehicle and configured to measure the surroundings of the automated vehicle and acquire two-dimensional point cloud data on a predetermined horizontal plane; a control unit that controls the traveling operation of the automated 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 autonomous 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 its 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 mounted on the autonomous vehicle and configured to acquire three-dimensional point cloud data by measuring the surroundings of the autonomous vehicle; a surroundings map creation unit that projects three-dimensional point cloud data within a predetermined range in the height direction, out of the three-dimensional point cloud data acquired by the second three-dimensional point cloud data acquisition unit, onto a horizontal plane to create a two-dimensional surroundings map of the surroundings of the autonomous vehicle; and An autonomous driving system comprising: a control unit that controls the driving operation of the autonomous vehicle 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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