Position estimation method for moving body, moving body, and program

Abstract

To improve accuracy of position estimation.SOLUTION: The position estimation method includes a step of acquiring first detection data by a first sensor that detects a detection target object present in a first detection range around a mobile body, a step of acquiring second detection data by a second sensor that detects a detection target object present in a second detection range different from the first detection range around the mobile body, a step of acquiring a first environment map for the first sensor created in advance for a traveling area of the mobile body and a second environment map for the second sensor created in advance for the traveling area, a step of creating an integrated map by integrating the first environment map and the second environment map based on at least one of the first detection data of the first sensor and the first detection range, and a step of estimating a position of the mobile body by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present disclosure relates to a method for estimating the position of a moving body, a moving body, and a program. [Background technology]

[0002] There are known mobile objects that move automatically and are equipped with sensors that detect their surroundings. For example, Patent Document 1 describes that obstacles are detected and a local map is generated that includes a travelable area in which the mobile object can travel and an obstacle area in which the obstacles are located. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-164424 Summary of the Invention [Problem to be solved by the invention]

[0004] The position of a moving object is estimated by comparing the sensor detection results with the environmental map data. If the sensor detection results have few features that match the environmental map data, the accuracy of the position estimation will decrease.

[0005] The present disclosure is intended to solve the above-mentioned problems, and aims to provide a method for estimating the position of a moving body, a moving body, and a program that can improve the accuracy of position estimation. [Means for solving the problem]

[0006] A position estimation method for a moving body according to the present disclosure is a position estimation method for a moving body that moves automatically, and includes the steps of acquiring first detection data by a first sensor that detects a detection object present in a first detection range around the moving body, acquiring second detection data by a second sensor that detects a detection object present in a second detection range around the moving body that is different from the first detection range, acquiring a first environmental map for the first sensor that is created in advance for the traveling area of ​​the moving body, and a second environmental map for the second sensor that is created in advance for the traveling area of ​​the moving body, creating an integrated map that integrates the first environmental map and the second environmental map based on at least one of the first detection data and the first detection range of the first sensor, and estimating the position of the moving body by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map.

[0007] The mobile body of the present disclosure is an automatically moving mobile body, and is equipped with a first sensor that detects detection objects present in a first detection range around the mobile body, a second sensor that detects detection objects present in a second detection range around the mobile body that is different from the first detection range, a memory unit that stores a first environmental map for the first sensor that is created in advance for the traveling area of ​​the mobile body, and a second environmental map for the second sensor that is created in advance for the traveling area of ​​the mobile body, an integration processing unit that creates an integrated map that integrates the first environmental map and the second environmental map based on at least one of the first detection data of the first sensor and the first detection range, and an estimation processing unit that performs processing to estimate the position of the mobile body by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map.

[0008] The program of the present disclosure is a program that causes a computer to execute a position estimation method for an automatically moving mobile body, and causes the computer to execute the following steps: acquiring first detection data from a first sensor that detects detection objects present in a first detection range around the mobile body; acquiring second detection data from a second sensor that detects detection objects present in a second detection range around the mobile body that is different from the first detection range; acquiring a first environmental map for the first sensor that is created in advance for the traveling area of ​​the mobile body, and a second environmental map for the second sensor that is created in advance for the traveling area of ​​the mobile body; creating an integrated map that integrates the first environmental map and the second environmental map based on at least one of the first detection data and the first detection range of the first sensor; and estimating the position of the mobile body by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map. [Effects of the Invention]

[0009] According to the present disclosure, the accuracy of position estimation can be improved. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic side view of a moving body according to an embodiment. [Figure 2] FIG. 2 is a schematic top view of the moving body according to the embodiment. [Figure 3] FIG. 3 is a schematic block diagram of a control device for a moving object. [Figure 4] FIG. 4 is a schematic diagram of a travel area for explaining an environmental map. [Figure 5] FIG. 5 is a diagram showing the first environmental map and the second environmental map. [Figure 6] FIG. 6 is a diagram showing an environmental map and the detection ranges of the sensors superimposed on each other. [Figure 7] FIG. 7 is a diagram illustrating the process of integrating the first environmental map and the second environmental map. [Figure 8]FIG. 8 is a schematic diagram illustrating the process of matching the detection data of the sensor with the integrated map. [Figure 9] FIG. 9 is a schematic diagram showing a method for checking the detection data of the sensor. [Figure 10] FIG. 10 is a flowchart illustrating the control flow of a moving body. [Figure 11] FIG. 11 is a diagram illustrating detection data when an obstacle is present. [Figure 12] FIG. 12 is a schematic diagram showing a method for determining an area in which the second environmental map according to the second embodiment is to be adopted. [Figure 13] FIG. 13 is a diagram illustrating an integrated map according to the second embodiment. [Figure 14] FIG. 14 is a diagram illustrating position estimation using an integrated map according to the second embodiment. [Figure 15] FIG. 15 is a schematic block diagram of a control device for a moving body according to the third embodiment. [Figure 16] FIG. 16 is a schematic diagram showing a situation in which luggage is present all around the moving object. [Figure 17] FIG. 17 is a schematic diagram illustrating the process of updating the environment map. [Figure 18] FIG. 18 is a schematic diagram showing the first environmental map and the second environmental map after updating. [Figure 19] FIG. 19 is a diagram illustrating position estimation using the updated environmental map. [Figure 20] FIG. 20 is a flowchart illustrating a control flow of a moving body according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the present disclosure is not limited to these embodiments, and when there are multiple embodiments, the present disclosure also includes configurations in which the respective embodiments are combined.

[0012] (First embodiment) (Mobile) Fig. 1 is a schematic side view of a moving body according to this embodiment. Fig. 2 is a schematic top view of the moving body according to this embodiment. The moving body 10 is a device that moves automatically (autonomous moving body).

[0013] In this embodiment, the moving body 10 is a non-holonomic system that cannot move sideways. In this embodiment, the moving body 10 is a device that can transport an object (luggage). More specifically, in this embodiment, the moving body 10 is a forklift, and more specifically, a so-called AGV (Automated Guided Vehicle) or AGF (Automated Guided Forklift). However, the moving body 10 is not limited to a forklift that transports an object, and may be any device that can move automatically.

[0014] In this embodiment, the mobile body 10 is used in a facility that is subject to logistics management, such as a warehouse, but the facility in which the mobile body 10 is operated is not particularly limited. The mobile body 10 picks up and transports objects placed within a travel area 60 set within the warehouse. The travel area 60 is an area where the objects are placed and where the mobile body 10 moves, such as the floor of the warehouse. A warehouse may have fixed objects such as shelves for storing the objects. In this embodiment, the objects transported by the mobile body 10 are objects in which cargo is loaded on a pallet. However, the objects are not limited to objects in which cargo is loaded on a pallet and may be in any form, for example, they may be cargo only without a pallet. Furthermore, the mobile body 10 is not limited to a device that transports objects and may be a device that moves within a facility for any purpose.

[0015] Hereinafter, one direction along the traveling area 60 is referred to as the X direction, and a direction along the traveling area 60 that intersects with the X direction is referred to as the Y direction. In this embodiment, the Y direction is a direction perpendicular to the X direction. The X and Y directions may also be referred to as directions along a horizontal plane. Furthermore, a direction perpendicular to the X and Y directions, more specifically, a direction pointing vertically upward, is referred to as the Z direction. Furthermore, in this embodiment, unless otherwise specified, "position" refers to a position (coordinate) in a coordinate system on a two-dimensional surface on the traveling area 60. Furthermore, unless otherwise specified, "attitude (orientation)" of the moving body 10 or the like refers to the orientation of the moving body 10 or the like in the coordinate system of the traveling area 60, and refers to the yaw angle (rotation angle) of the moving body 10 when viewed from the Z direction, with the X direction being 0°.

[0016] As shown in FIGS. 1 and 2 , the mobile body 10 includes a vehicle body 20, wheels 20A, straddle legs 21, a mast 22, a fork 24, a first sensor 25, a second sensor 26, an inertial measurement unit 27, and a control device 28. The straddle legs 21 are provided at one end of the vehicle body 20 in the longitudinal direction and are a pair of shaft-shaped members protruding from the vehicle body 20. The wheels 20A are provided at the tip of each straddle leg 21 and on the vehicle body 20. A total of three wheels 20A are provided, but the positions and number of the wheels 20A may be arbitrary. The mast 22 is movably attached to the straddle legs 21 and moves in the longitudinal direction of the vehicle body 20. The mast 22 extends in the vertical direction. The fork 24 is attached to the mast 22 so as to be movably attached in the vertical direction. The fork 24 may also be movable in the lateral direction of the vehicle body 20 (a direction intersecting the up-down direction and the fore-and-aft direction) relative to the mast 22. The fork 24 has a pair of claws 24A, 24B. The claws 24A, 24B extend from the mast 22 toward the rear of the vehicle body 20. The claws 24A and 24B are arranged apart from each other in the lateral direction of the mast 22. Hereinafter, in the fore-and-aft direction, the direction on the side of the vehicle 10 where the fork 24 is not provided will be referred to as the forward direction, and the direction on the side where the fork 24 is provided will be referred to as the rearward direction.

[0017] The first sensor 25 is disposed at a first height position H1 of the moving body 10. The height position is defined as the distance upward from the lowest point of the moving body 10 (i.e., the point where the wheel 20A touches the ground). The first height position H1 is at the top of the moving body 10. The first sensor 25 is installed at the upper end of the support member 23 that extends upward from the vehicle body 20. The first height position H1 is near the upper end of the mast 22. However, the position at which the first sensor 25 is provided is not limited to this, and the first sensor 25 may be provided at any position, and the number of first sensors 25 provided may also be arbitrary.

[0018] The first sensor 25 detects a detection object that exists around the moving object 10. The first sensor 25 detects at least a detection object that exists around the moving object 10 in an in-plane direction parallel to the surface (floor surface) that constitutes the traveling area 60. In other words, the first sensor 25 has a detection range that is at least along the horizontal direction. As shown in FIG. 2, the first sensor 25 detects a detection object that exists in a first detection range 75 around the moving object 10.

[0019] The first sensor 25 is, for example, a sensor that emits laser light. The first sensor 25 emits laser light while scanning in a predetermined direction and detects the position of the detection target from the reflected light of the emitted laser light. In other words, the first sensor 25 can also be said to be a so-called two-dimensional (2D)-LiDAR (Light Detection and Ranging). In this embodiment, the first detection range 75 is a range irradiated with laser light that is scanned in a rotational direction around an axis in the vertical direction, with the moving object 10 as the center. Therefore, the first detection range 75 can be defined by a scanning angle θ1 around the axis in the vertical direction. The scanning angle θ1 can take any value within the range of 0 degrees < θ1 ≦ 360 degrees, but in the example shown in FIG. 2, the scanning angle θ1 is approximately 270 degrees. The first detection range 75 includes ranges in front, left, and right around the moving object 10.

[0020] The first sensor 25 mainly detects the wall surfaces that divide the travel area 60 and sign members placed at high places in the travel area 60. The sign members have reflective parts that reflect laser light, and are placed at predetermined locations to serve as landmarks that indicate specific positions in the travel area 60.

[0021] The second sensor 26 is disposed at a second height position H2 of the moving body 10. The second height position H2 is a position different from the first height position H1. In this embodiment, the second height position H2 is a position lower than the first height position H1. The second height position H2 is a lower part of the moving body 10. The second height position H2 is near the underside of the moving body 10. The second sensors 26 are provided at the rear end of each straddle leg 21 and at each of the left and right ends of the front of the vehicle body 20. That is, the second sensors 26 are disposed at the four corners of the moving body 10 in a plan view. Hereinafter, the second sensor 26 on the front side of the moving body 10 will be referred to as second sensor 26F, and the second sensor 26 on the rear side of the moving body 10 will be referred to as second sensor 26R. However, the positions at which the second sensors 26 are disposed are not limited thereto, and the second sensors 26 may be disposed at any position, and the number of second sensors 26 provided may also be arbitrary.

[0022] The second sensor 26 detects detection objects that exist around the moving object 10. The second sensor 26 detects detection objects that exist around the moving object 10 at least in an in-plane direction parallel to the surface (floor surface) that constitutes the traveling area 60. In other words, the second sensor 26 has a detection range that is at least along the horizontal direction. As shown in FIG. 2 , the second sensor 26 detects detection objects that exist in a second detection range 76 around the moving object 10.

[0023] The second sensor 26 is, for example, a sensor that emits laser light. The second sensor 26 emits laser light while scanning in a predetermined direction and detects the position of the detection target from the reflected light of the emitted laser light. In other words, the second sensor 26 can also be considered a so-called two-dimensional (2D) LiDAR. In this embodiment, the second detection range 76 is the irradiation range of the laser light that is scanned in a rotational direction around an axis in the vertical direction, with the moving object 10 as the center. Furthermore, in this embodiment including multiple second sensors 26, the second detection range 76 is the combined range of the detection ranges of the individual second sensors 26. The second detection range 76 can be defined by a scanning angle θ2 around the axis in the vertical direction. The scanning angle θ2 can take any value within the range of 0 degrees < θ2 ≦ 360 degrees. In the example shown in FIG. 2, the scanning angle θ2 is approximately 200 degrees. The second detection range 76 (referred to as second detection range 76F) of the two front second sensors 26F includes the ranges in front and to the left and right around the moving object 10. The second detection range 76 (hereinafter referred to as second detection range 76R) of the two rear second sensors 26R includes ranges behind and to the left and right of the periphery of the moving object 10. The second detection ranges 76F and 76R together cover the entire circumference (i.e., 360 degrees) of the moving object 10. It is preferable that the second sensors 26 as a whole (here, the second sensors 26F and 26R combined) be able to detect the entire circumference of the moving object 10.

[0024] The second sensor 26 mainly detects obstacles that exist on the floor surface of the travel area 60 and that may come into contact with the moving object 10.

[0025] The first detection range 75 of the first sensor 25 and the second detection range 76 of the second sensor 26 are different. In the example of FIG. 2, the first detection range 75 is a range of a scanning angle θ1 (approximately 270 degrees) centered on the moving object 10, and the second detection range 76 includes an angular range centered on the moving object 10 that is not included in the first detection range 75. In other words, the first detection range 75 includes both the front and left and right sides of the moving object 10, but does not include the rear of the moving object 10. The second detection range 76R includes a range of approximately 200 degrees behind and to the left and right of the moving object 10. The first detection range 75 and the second detection range 76R include an overlapping range 77. The second detection range 76R overlaps with the first detection range 75 at one end and the other end of the angular range of the scanning angle θ2. Therefore, second detection range 76F and second detection range 76R can detect the entire circumference (360 degrees) of moving object 10, and in addition, first detection range 75 and second detection range 76R can be combined to detect the entire circumference (360 degrees) of moving object 10. Second detection range 76F includes a range of approximately 200 degrees in front of, and to the left and right of, moving object 10. Second detection range 76F is included in first detection range 75 and has a narrower angular range than first detection range 75.

[0026] The first sensor 25 and the second sensor 26 are not limited to two-dimensional LiDAR and may be sensors that detect objects in any manner, for example, so-called three-dimensional (3D) LiDAR that scans in multiple directions, so-called one-dimensional (1D) LiDAR that does not scan, or a camera.

[0027] The inertial measurement unit 27 is a device that measures the acceleration and angular velocity of the mobile body 10, and is also called an IMU (Inertial Measurement Unit). The inertial measurement unit 27 outputs data on the acceleration of each of three orthogonal axes and the angular velocity around each axis in a mobile body coordinate system fixed to the inertial measurement unit 27. Based on the output data of the inertial measurement unit 27 over time from a certain point in time, it is possible to calculate changes in the position and orientation of the mobile body 10 from that point in time.

[0028] (Control device) The control device 28 controls the movement of the moving body 10. Fig. 3 is a schematic block diagram of the control device of the moving body.

[0029] The control device 28 is a computer and includes a communication unit 30, a storage unit 32, a control unit 34, and an interface 36. The communication unit 30 is a module used by the control unit 34 to communicate with external devices, and may include, for example, an antenna. In this embodiment, the communication method used by the communication unit 30 is wireless communication, but any communication method may be used.

[0030] The storage unit 32 is a memory that stores various information such as the calculation contents and programs of the control unit 34, and includes, for example, a main storage device such as a random access memory (RAM) and a read-only memory (ROM), and an external storage device such as a hard disk drive (HDD) or a solid state drive (SSD). The storage unit 32 stores a first environmental map 41 and a second environmental map 42. The first environmental map 41 is an environmental map for the first sensor 25 that is created in advance for the travel area 60 of the mobile object 10. The second environmental map 42 is an environmental map for the second sensor 26 that is created in advance for the travel area 60 of the mobile object 10. The storage unit 32 stores an integrated map 44 that is generated by an integration processing unit 54 (described later). The storage unit 32 also stores a program 46 that causes the computer to function as the control device 28.

[0031] The interface 36 has an input / output circuit for transmitting and receiving information to and from each device provided in the moving body 10, such as the first sensor 25, the second sensor 26, and the inertial measurement unit 27.

[0032] The control unit 34 is a computing device and includes a computing circuit such as a CPU (Central Processing Unit). The control unit 34 includes a movement control unit 50, a detection control unit 52, an integration processing unit 54, and an estimation processing unit 56. The control unit 34 reads and executes a program 46 (software) from the storage unit 32, thereby realizing the movement control unit 50, the detection control unit 52, the integration processing unit 54, and the estimation processing unit 56 and performing these processes. The control unit 34 may perform these processes using a single CPU, or may be provided with multiple CPUs and perform the processes using the multiple CPUs. Furthermore, at least a portion of the movement control unit 50, the detection control unit 52, the integration processing unit 54, and the estimation processing unit 56 may be realized by hardware circuits. Furthermore, the program 46 stored in the storage unit 32 may be stored in a recording medium readable by the control device 28.

[0033] The control unit 34 acquires work order information, which specifies the location of the transported object and the location of the destination, from a management device (not shown) via the communication unit 30. The management device is a system that manages logistics in the facility. In the embodiment, the management device is a WCS (Warehouse Control System) or WMS (Warehouse Management System). However, it is not limited to a WCS or WMS and may be any system, such as a back-end system like other production management systems. The movement control unit 50 controls the movement mechanisms of the mobile object 10, such as the drive unit and steering, to control the movement of the mobile object 10. The detection control unit 52 acquires detection data from the first sensor 25, the second sensor 26, and the inertial measurement unit 27. The integration processing unit 54 creates an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42. The estimation processing unit 56 performs processing to estimate the position of the moving body 10 by comparing the first detection data 71 (see Figure 6) of the first sensor 25 and the second detection data 72 (see Figure 6) of the second sensor 26 with the integrated map 44.

[0034] (Environmental Map) Fig. 4 is a schematic diagram of a travel area to explain the environmental map. Fig. 5 is a diagram showing a first environmental map and a second environmental map. The environmental map is map data for estimating the position of the mobile body 10 by comparing it with the detection data of the first sensor 25 and the second sensor 26. The environmental map is created in advance. The environmental map defines the shape of the travel area 60 of the mobile body 10.

[0035] The first environmental map 41 is compared with first detection data 71 obtained by detecting a detection target in the travel area 60 by the first sensor 25. The first environmental map 41 defines the shape of the travel area 60 within a plane including the first height position H1 of the mobile object 10. That is, the first environmental map 41 represents a set of points where the laser scanning plane of the first sensor 25 at the first height position H1 intersects with the detection target in the travel area 60. The second environmental map 42 is compared with second detection data 72 obtained by detecting a detection target in the travel area 60 by the second sensor 26. The second environmental map 42 defines the shape of the travel area 60 within a plane including the second height position H2 of the mobile object 10. That is, the second environmental map 42 represents a set of points where the laser scanning plane of the second sensor 26 at the second height position H2 intersects with the detection target in the travel area 60.

[0036] The first environmental map 41 and the second environmental map 42 may differ in content due to differences in the height positions at which the sensors are installed. In the example of FIG. 4, the travel area 60 is a warehouse surrounded by a floor 61 and a wall 62. A step 63 surrounds the periphery of the floor 61 of the travel area 60. The first sensor 25 (first height position H1) is located above the top surface of the step 63, and the second sensor 26 (second height position H2) is located below the top surface of the step 63. A pillar 64 is located at a corner of the travel area 60. In this case, as shown in FIG. 5, the first environmental map 41 shows a contour shape 66A of the pillar 64 and the wall 62 in the laser scanning plane of the first sensor 25. The second environmental map 42 shows a contour shape 66B of the step 63 in the laser scanning plane of the second sensor 26.

[0037] 6 is a diagram showing an environmental map and the detection ranges of each sensor superimposed on it. For convenience, the second detection data and second detection range of the front second sensor 26F are omitted from the illustration. In each diagram, the point cloud detected by the first sensor 25 is indicated by circular dots, and the point cloud detected by the second sensor 26 is indicated by diamond-shaped dots.

[0038] The detection control unit 52 causes the first sensor 25 to emit laser light. The detection control unit 52 acquires first detection data 71 consisting of a point cloud MA based on the detection result of the reflected light received by the first sensor 25. The first detection data 71 by the first sensor 25 is data corresponding to the portion of the contour shape 66A of the first environmental map 41 that is included in the first detection range 75 of the first sensor 25.

[0039] The detection control unit 52 causes the second sensor 26 to emit laser light. The detection control unit 52 acquires second detection data 72 consisting of a point cloud MB based on the detection result of the reflected light received by the second sensor 26. The second detection data 72 by the second sensor 26 is data corresponding to the portion of the contour shape 66B of the second environmental map 42 that is included in the second detection range 76 of the second sensor 26.

[0040] The point clouds MA and MB can be said to be measurement points that indicate the positions where the laser light hit. In the example of Fig. 6, the detection control unit 52 acquires the point cloud MA that indicates the positions of the walls 62 and pillars 64 in front and on the left and right of the moving body 10, and the point cloud MB that indicates the positions of the steps 63 behind and on the left and right of the moving body 10.

[0041] (Creating an integrated map) 7 is a diagram illustrating the integration process of the first environmental map and the second environmental map. The integration processing unit 54 creates an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on at least one of the first detection data 71 and the first detection range 75 of the first sensor 25. In the first embodiment, the integration processing unit 54 integrates the first environmental map 41 and the second environmental map 42 based on the first detection range 75 of the first sensor 25.

[0042] Specifically, the integration processing unit 54 creates the integrated map 44 by adopting the first environmental map 41 for areas of the traveling area 60 of the moving object 10 that are included in the first detection range 75, and adopting the second environmental map 42 for areas that are not included in the first detection range 75. The first detection range 75 is a range of a scanning angle θ1 that includes the front and both left and right directions of the moving object 10, but does not include the rear of the moving object 10. On the other hand, the second detection range 76R is a range of a scanning angle θ2 that includes the rear and both left and right directions of the moving object 10. Therefore, the integration processing unit 54 creates the integrated map 44 by integrating the first environmental map 41 of the area included in the first detection range 75 with the second environmental map 42 of the rear area that is not included in the first detection range 75.

[0043] The integration processing unit 54 acquires the predicted position of the moving body 10 at the time of acquiring the first detection data 71 and the second detection data 72 based on the previously estimated position of the moving body 10 and movement data of the moving body 10 (measurement data of the inertial measurement unit 27), and acquires the area included in the first detection range 75 at the predicted position. Specifically, the integration processing unit 54 acquires the previous estimated position of the moving body 10 estimated by the estimation processing unit 56. The estimated position includes six-degree-of-freedom parameters consisting of positions in the directions of each coordinate axis (X, Y, Z) in the coordinate system of the traveling area 60 and rotation angles around each coordinate axis. Here, it is assumed that the floor surface 61 of the traveling area 60 is horizontal (parallel to the XY plane). It is sufficient to acquire three parameters, namely, the X coordinate, Y coordinate, and rotation angle around the Z axis (yaw angle) of the moving body 10 as the estimated position.

[0044] The integration processing unit 54 acquires measurement data from the inertial measurement unit 27 from the most recent estimation time point by the estimation processing unit 56 to the current time point. The integration processing unit 54 calculates the change in position of the mobile object 10 from the most recent estimated position to the current time point using time series data of the accelerations in each of the three orthogonal axes and the angular velocity around each axis obtained from the inertial measurement unit 27. The integration processing unit 54 calculates the predicted position of the mobile object 10 at the time point (current time point) when the first detection data 71 and the second detection data 72 were acquired using the most recent estimated position by the estimation processing unit 56 and the position change from the estimation time point to the current time point calculated from the measurement data of the inertial measurement unit 27. The predicted position of the mobile object 10 is not based on a comparison between the most recent first detection data 71 and the most recent second detection data 72 and the integrated map 44, and therefore can be considered a rough estimation result for creating the integrated map 44.

[0045] The integration processing unit 54 identifies an area of ​​the traveling area 60 that is included in the first detection range 75 from the obtained predicted position of the moving object 10, and extracts first partial map data 81 of the identified area from the first environmental map 41. The integration processing unit 54 extracts second partial map data 82 for an area of ​​the traveling area 60 that is not included in the first detection range 75 from the second environmental map 42. The integration processing unit 54 creates the integrated map 44 by combining the extracted first partial map data 81 and second partial map data 82. In this embodiment, because the first detection range 75 and the second detection range 76R include an overlapping range 77, either the first partial map data 81 or the second partial map data 82 can be used in the overlapping range 77. The integration processing unit 54 may use the first partial map data 81, the second partial map data 82, or both map data for the overlapping range 77. In this embodiment, the integration processing unit 54 adopts the first partial map data 81 for the overlapping range 77.

[0046] Note that the creation of the integrated map by the integration processing unit 54 includes, as described above, combining the first partial map data 81 and the second partial map data 82, or overwriting areas of the set of points constituting the first environmental map 41 that are not included in the first detection range 75 with the set of points constituting the second environmental map 42, but does not necessarily involve processes such as overwriting, changing, correcting, combining, or merging the data constituting the first environmental map 41 and the second environmental map 42. In other words, as long as the area of ​​the first environmental map 41 against which the first detection data 71 of the first sensor 25 should be compared (first partial map data 81) and the area of ​​the second environmental map 42 against which the second detection data 72 of the second sensor 26 should be compared (second partial map data 82) are identified so as to enable the position estimation process described below, it can be considered that an integrated map has essentially been created.

[0047] As described above, in this embodiment, the integration processing unit 54 acquires the predicted position of the moving object 10 at the time of acquiring the first detection data 71 and the second detection data 72, and integrates the first partial map data 81 and the second partial map data 82 at the predicted position to create the integrated map 44. The integration processing unit 54 creates the integrated map 44 every time it acquires the first detection data 71 and the second detection data 72 and estimates the position of the moving object 10.

[0048] (Position estimation) Fig. 8 is a schematic diagram illustrating the process of matching sensor detection data with the integrated map. Fig. 9 is a schematic diagram illustrating a method of matching sensor detection data. The estimation processing unit 56 performs a process of estimating the position of the moving object 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44. The estimation processing unit 56 matches the first detection data 71 of the first sensor 25 with the first partial map data 81 of the integrated map 44. The estimation processing unit 56 matches the second detection data 72 of the second sensor 26 with the second partial map data 82 of the integrated map 44.

[0049] The estimation processing unit 56 uses pattern matching processing to determine the degree of match between the point clouds included in the detection data and the integrated map 44. As shown in Fig. 9, the estimation processing unit 56 translates and rotates the point cloud MA of the first detection data 71 and the point cloud MB of the second detection data 72 relative to the integrated map 44 to calculate the amount of translation and rotation that maximizes the degree of match between the point clouds MA and MB and the first partial map data 81 and the second partial map data 82. As a result, the estimation processing unit 56 calculates the amount of translation and rotation when the point clouds MA and MB match the first partial map data 81 and the second partial map data 82, as shown in Fig. 8. The estimation processing unit 56 acquires an estimated position (X coordinate, Y coordinate, yaw angle) of the moving object 10 in the coordinate system of the traveling area 60 based on the obtained amount of translation and rotation.

[0050] As a result, the point cloud MB of the second detection data 72 is matched with the second partial map data 82 even in areas that are blind spots of the first sensor 25. Since the number of sample points for the pattern matching process increases, the accuracy of position estimation improves accordingly. In addition, the environmental map includes data that serves as features for determining the estimated position of the moving body 10, such as walls 62 and pillars 64 that indicate the outline of the traveling area 60. Even in cases where the features for determining the estimated position exist only in the blind spots of the first sensor 25, position estimation is possible by matching the point cloud MB of the second detection data 72 with the second partial map data 82.

[0051] (Processing flow) The control flow of the mobile body described above will now be described. FIG. 10 is a flowchart illustrating the control flow of the mobile body. As shown in FIG. 10, the detection control unit 52 acquires first detection data 71 by the first sensor 25, which detects a detection target present in a first detection range 75 around the mobile body 10 (step S10). The detection control unit 52 acquires second detection data 72 by the second sensor 26, which detects a detection target present in a second detection range 76 around the mobile body 10 (step S11). Note that step S10 and step S11 may be performed either first or simultaneously. The integration processing unit 54 acquires, from the storage unit 32, a first environmental map 41 for the first sensor 25, which is created in advance for the traveling area 60 of the mobile body 10, and a second environmental map 42 for the second sensor 26, which is created in advance for the traveling area 60 of the mobile body 10 (step S12). The integration processing unit 54 creates an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on the first detection range 75 of the first sensor 25 (step S13). The estimation processing unit 56 estimates the position of the moving object 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44 (step S14).

[0052] (effect) As described above, in this embodiment, the integrated map 44 is created by integrating the first environmental map 41 and the second environmental map 42 based on the first detection range 75 of the first sensor 25, and the position of the moving object 10 is estimated by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44. As a result, even if there is a feature that can be matched with the environmental map in a location that cannot be detected by the first sensor 25, for example, a location that is a blind spot of the first sensor 25, the second detection data 72 of the second sensor 26 can be used to match with the integrated map 44. As a result, the accuracy of estimating the position of the moving object 10 is improved.

[0053] (Second embodiment) Next, a second embodiment will be described. The second embodiment differs from the first embodiment in the method of creating the integrated map 44. In the second embodiment, a description of parts that are common to the first embodiment will be omitted.

[0054] FIG. 11 illustrates detection data when an obstacle is present. As described above, the environmental map includes characteristic data for determining the estimated position of the mobile object 10, such as a wall 62 defining the outline of the travel area 60. However, as shown in FIG. 11, the presence of a shelf 67, luggage G, or the like on the sensor's scanning plane may block the sensor's laser light, making it impossible to detect characteristic portions of the environmental map. FIG. 11 illustrates an example in which the first environmental map 41 includes the outline of the wall 62, and pattern matching is performed using a point cloud along the wall 62. In this case, the estimation processing unit 56 excludes point clouds MG included in the first detection data 71 that significantly deviate from the first environmental map 41 from the matching process. As a result, the number of points in the first detection data 71 that match the first environmental map 41 decreases, resulting in a decrease in the accuracy of position estimation.

[0055] Therefore, in the second embodiment, the integration processing unit 54 creates an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on the first detection data 71 of the first sensor 25. Specifically, the integration processing unit 54 creates the integrated map 44 by adopting the first environmental map 41 for areas where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and adopting the second environmental map 42 for areas NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range. Note that in the second embodiment, an example will be described in which processing for creating the integrated map 44 based on the first detection range 75 of the first sensor 25 is also performed, as in the first embodiment.

[0056] FIG. 12 is a schematic diagram illustrating a method for determining an area for adopting a second environmental map according to the second embodiment. As shown in FIG. 12, the integration processing unit 54 acquires first detection data 71 from the first sensor 25. It is assumed that the point cloud of the acquired first detection data 71 includes a point MC that is blocked by an obstacle, such as luggage G, on the scanning surface of the first sensor 25. The integration processing unit 54 acquires a first environmental map 41 from the storage unit 32. The first environmental map 41 defines a contour shape 66A of a wall 62 and a pillar 64 on the scanning surface of the first sensor 25. The integration processing unit 54 compares the point cloud of the first detection data 71 with the contour shape 66A of the first environmental map 41 and determines whether the degree of match with the first environmental map 41 for each point included in the point cloud is within an acceptable range.

[0057] That is, the integration processing unit 54 obtains the predicted position of the mobile object 10 at the time of acquisition of the first detection data 71 and the second detection data 72 based on the immediately preceding estimated position by the estimation processing unit 56 and the position change up to the present time calculated from the measurement data of the inertial measurement unit 27. From the predicted position, it is possible to determine whether the position of each point included in the point cloud of the first detection data 71 significantly deviates from the contour shape 66A of the first environmental map 41. For example, if point P in FIG. 12 is not obstructed by baggage G, it would be detected as point Q on the contour shape 66A of the first environmental map 41. The integration processing unit 54 excludes data of point P that deviates from the predicted position by a larger amount than the allowable range of positional deviation allowed from the first detection data 71. Similarly, the integration processing unit 54 excludes point MC that is obstructed by an obstacle from the first detection data 71. The integration processing unit 54 determines the range including each point MC excluded from the first detection data 71 as the area NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the allowable range. For example, the integration processing unit 54 may determine the angular range between a point at one end and a point at the other end of the scanning direction (rotation direction around the Z axis) of the first sensor 25 among the points MC as the area NA. The integration processing unit 54 creates the integrated map 44 so as to adopt the second environmental map 42 for the determined area NA.

[0058] FIG. 13 is a diagram illustrating an integrated map according to the second embodiment. The integration processing unit 54 identifies an area where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range (i.e., an area where the laser light is not blocked by an obstacle) and extracts first partial map data 91 for the identified area from the first environmental map 41. The integration processing unit 54 identifies an area NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range and extracts second partial map data 92 for the identified area NA from the second environmental map 42. In the example of FIG. 13, similar to the first embodiment, the integration processing unit 54 identifies an area NB not included in the first detection range 75 of the first sensor 25 (an area in the blind spot of the first sensor 25) and extracts second partial map data 93 for the identified area NB from the second environmental map 42. In this manner, the integration processing unit 54 creates an integrated map 44 including the first partial map data 91, the second partial map data 92, and the second partial map data 93.

[0059] (Position estimation) 14 is a diagram illustrating position estimation using an integrated map according to the second embodiment. The estimation processing unit 56 performs processing to estimate the position of the moving object 10 by comparing the first detection data 71 from the first sensor 25 and the second detection data 72 from the second sensor 26 with the integrated map 44. The estimation processing unit 56 compares the first detection data 71 from the first sensor 25 with the first partial map data 91 from the integrated map 44. The estimation processing unit 56 compares the second detection data 72 from the second sensor 26 with the second partial map data 92 and the second partial map data 93 from the integrated map 44. As a result, the estimation processing unit 56 calculates the amounts of translation and rotation of the point clouds such that the point cloud 95A included in the first detection data 71 from the first sensor 25 matches the first partial map data 91, the point cloud 95B included in the second detection data 72F from the second sensor 26F matches the second partial map data 92, and the point cloud 95C included in the second detection data 72R from the second sensor 26R matches the second partial map data 93. The estimation processing unit 56 obtains an estimated position of the moving object 10 in the coordinate system of the travel area 60 based on the obtained translation amount and rotation amount.

[0060] (Processing flow) The control flow of the moving body according to the second embodiment is basically the same as the flowchart shown in Fig. 10. In the second embodiment, in step S13 of creating the integrated map 44, the integration processing unit 54 creates the integrated map 44 so as to adopt the first environmental map 41 for areas where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and to adopt the second environmental map 42 for areas NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range.

[0061] As described above, in this embodiment, the integrated map 44 is created so that the second environmental map 42 is used for the area NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range. When a point cloud matching the first environmental map 41 cannot be obtained in the area NA where the light of the first sensor 25 is blocked by baggage G, the point cloud 95B included in the second detection data 72F for the area NA can be compared with the second partial map data 92, ensuring a sufficient number of sample points for pattern matching. Therefore, even when the first detection data 71 of the first sensor 25 cannot be properly obtained due to an obstacle, the accuracy of the position estimation of the moving object 10 can be maintained. Furthermore, in this embodiment, the integrated map 44 is created so that the second environmental map 42 is used for the area NB not included in the first detection range 75. Therefore, even in a blind spot of the first sensor 25, the second detection data 72 of the second sensor 26 can be used to compare the integrated map 44. As a result, the accuracy of the position estimation of the moving object 10 is improved.

[0062] In the second embodiment, similarly to the first embodiment, the integration processing unit 54 creates the integrated map 44 based on the first detection range 75 of the first sensor 25 so as to include the second partial map data 93 of the area NB that is not included in the first detection range 75 of the first sensor 25. However, the second partial map data 93 of the area NB does not have to be included in the integrated map 44. That is, in the second embodiment, the integrated map 44 is created based on both the first detection data 71 and the first detection range 75 of the first sensor 25. However, the integrated map 44 may be created based only on the first detection data 71 of the first detection data 71 and the first detection range 75.

[0063] (Third embodiment) Next, a third embodiment will be described. The third embodiment differs from the second embodiment in that the environmental map is updated using detection data from sensors. Explanations of the configurations of the third embodiment that are common to the second embodiment will be omitted.

[0064] 15 is a schematic block diagram of a control device for a moving body according to the third embodiment. In the third embodiment, the control unit 34 of the moving body 10 further includes an update processing unit 58 in addition to a movement control unit 50, a detection control unit 52, an integration processing unit 54, and an estimation processing unit 56.

[0065] The update processing unit 58 updates the environmental map stored in the storage unit 32 using the sensor detection data. Specifically, the update processing unit 58 updates the first environmental map 41 based on a plurality of first detection data 71 acquired at different positions, and updates the second environmental map 42 based on a plurality of second detection data 72 acquired at different positions.

[0066] FIG. 16 is a schematic diagram showing a situation in which luggage is present all around the mobile object. FIG. 16(A) is a diagram showing a travel area 60 as viewed from above, and FIG. 16(B) is a side view of the mobile object 10 in the travel area 60. FIG. 16 illustrates a situation in which the mobile object 10 is surrounded by shelves 67 on which luggage G is placed in a warehouse. The shelves 67 have two levels of storage space, an upper level and an lower level. The scanning plane of the first sensor 25 contains luggage G placed on the upper level of the shelves 67, and the scanning plane of the second sensor 26 contains luggage G placed on the lower level of the shelves 67. In this case, the first sensor 25 and the second sensor 26 may barely be able to detect the contour shape 66A of the first environmental map 41 or the contour shape 66B of the second environmental map 42, which may make it difficult to estimate the position of the mobile object 10.

[0067] Therefore, in the third embodiment, when the estimation processing unit 56 estimates the position of the moving body 10, the update processing unit 58 identifies obstacles based on the first detection data 71 and the second detection data 72 detected during the position estimation, and adds data of the identified obstacles to the first environmental map 41 and the second environmental map 42.

[0068] 17 is a schematic diagram illustrating the process of updating the environmental map. As shown in FIG. 17, the position of the moving object 10 is estimated when the moving object 10 is at position A1. The method of position estimation is the same as that in the second embodiment. At this time, a package G1 present on the scanning plane of the first sensor 25 and a package G2 present on the scanning plane of the second sensor 26 become obstacles that block the light of the respective sensors.

[0069] The integration processing unit 54 identifies an area where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and extracts first partial map data 91 of the identified area from the first environmental map 41. The integration processing unit 54 identifies an area NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range, and extracts second partial map data 92 of the identified area NA from the second environmental map 42. The integration processing unit 54 also identifies an area NB that is not included in the first detection range 75 of the first sensor 25 (an area that is a blind spot of the first sensor 25), and extracts second partial map data 93 of the identified area NB from the second environmental map 42. As a result, the integration processing unit 54 creates an integrated map 44 that includes the first partial map data 91, the second partial map data 92, and the second partial map data 93.

[0070] When the moving object 10 moves from position A1 to position A2, the detection control unit 52 acquires first detection data 71 and second detection data 72 from the first sensor 25 and the second sensor 26, respectively. As a result, an obstacle point cloud 105A indicating the outline of the baggage G1 present on the scan plane of the first sensor 25 is acquired from the first detection data 71 during travel. An obstacle point cloud 105B indicating the outline of the baggage G2 present on the scan plane of the second sensor 26 is acquired from the second detection data 72 during travel.

[0071] FIG. 18 is a schematic diagram showing the first environmental map 41 and the second environmental map 42 after updating. The update processing unit 58 adds obstacle data 106A based on the acquired obstacle point cloud 105A to the first environmental map 41. The update processing unit 58 adds obstacle data 106B based on the acquired obstacle point cloud 105B to the second environmental map 42. In addition to the features of the wall 62, the pillar 64, and the step 63, the first environmental map 41 and the second environmental map 42 now include contour shapes indicating the obstacles (baggage G1, baggage G2) based on the obstacle point cloud 105A and the obstacle point cloud 105B. The update processing unit 58 updates the first environmental map 41 to which the obstacle data 106A has been added and the second environmental map 42 to which the obstacle data 106B has been added in the storage unit 32. The updated first environmental map 41 and the second environmental map 42 are used for the next position estimation of the moving object 10.

[0072] Fig. 19 is a diagram for explaining position estimation using the updated environmental map, showing a situation in which the moving object 10 performs position estimation after arriving at position A2.

[0073] The integration processing unit 54 identifies an area where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and extracts first partial map data 91 of the identified area from the first environmental map 41. In FIG. 19, the contour shape representing the baggage G1 is included in the updated first environmental map 41, so the point cloud including the obstacle point cloud 105A matches the first environmental map 41. In other words, the baggage G1 can be used as a feature of the environmental map used for estimating the position of the moving object 10, and therefore, even if the light of the first sensor 25 is blocked by the baggage G1, the data (obstacle point cloud 105A) can be used for position estimation.

[0074] Furthermore, at position A2, it is assumed that the light of the first sensor 25 is blocked by a new package G3. Package G3 is a package that was hidden behind package G1 and was not reached by the laser light of the first sensor 25 from position A1 shown in FIG. 17 . The integration processing unit 54 identifies an area NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the allowable range due to the light being blocked by package G3, and extracts second partial map data 102 for the identified area NA from the second environmental map 42. The area NA includes package G2. Therefore, the integration processing unit 54 extracts second partial map data 102 including obstacle data 106B from the updated second environmental map 42. As a result, the integration processing unit 54 creates an integrated map 44 that includes the first partial map data 101 including the obstacle data 106A, the second partial map data 102 including the obstacle data 106B, and the second partial map data 103 for the area NB.

[0075] The estimation processing unit 56 performs position estimation using the integrated map 44 created at position A2. After the position estimation is performed, the update processing unit 58 acquires obstacle data based on the obstacle point cloud 105C indicating the outline of the baggage G3 present on the scan plane of the first sensor 25, and adds the acquired obstacle data to the first environmental map 41 to update it. In the third embodiment, the environmental map is updated in this manner.

[0076] Note that when loading and unloading luggage G in a warehouse, luggage G is placed on and removed from the shelves 67 as the work progresses, so the presence or absence of luggage G changes over time. When an obstacle point cloud corresponding to obstacle data added to the environmental map is no longer detected in the first detection data 71, the update processing unit 58 deletes the obstacle data from the environmental map and stores the environmental map from which the obstacle data has been deleted in the storage unit 32, thereby updating the environmental map. For example, when an obstacle point cloud 105A indicating the outline of luggage G1 is no longer acquired in the first detection data 71, the update processing unit 58 deletes obstacle data 106A based on the obstacle point cloud 105A from the first environmental map 41. As a result, the first environmental map 41 and the second environmental map 42 are maintained in a state that reflects the latest situation of the traveling area 60.

[0077] In this way, in the third embodiment, the first environmental map 41 and the second environmental map 42 are updated, so that obstacles present around the moving body 10 can be used as features of the environmental map to be used for position estimation. As a result, even if an obstacle exists around the moving body 10, it is possible to avoid a situation where position estimation becomes impossible.

[0078] (Processing flow) FIG. 20 is a flowchart illustrating the control flow of a moving object according to the third embodiment. In the third embodiment, in addition to steps S10 to S14, step S15 is further included. The processes of steps S10 to S14 are the same as those of the second embodiment. In step S15, the update processing unit 58 updates the first environmental map 41 based on a plurality of first detection data 71 acquired at different positions, and updates the second environmental map 42 based on a plurality of second detection data 72 acquired at different positions. Thereafter, at the next position estimation timing, the integration processing unit 54 acquires the updated first environmental map 41 and the updated second environmental map 42 from the storage unit 32 (step S12). The integration processing unit 54 creates an integrated map 44 by adopting the first environmental map 41 for areas where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and adopting the second environmental map 42 for areas NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range (step S13). The estimation processing unit 56 estimates the position of the moving body 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44 (step S14). As a result, the position of the moving body 10 is estimated based on the updated first environmental map 41 and the updated second environmental map 42.

[0079] (effect) The method for estimating the position of a moving body according to a first aspect of the present disclosure is a method for estimating the position of a moving body 10 that moves automatically, and includes a step S10 of acquiring first detection data 71 by a first sensor 25 that detects a detection object that exists in a first detection range 75 around the moving body 10, a step S11 of acquiring second detection data 72 by a second sensor 26 that detects a detection object that exists in a second detection range 76 different from the first detection range 75 around the moving body 10, and a step S12 of acquiring second detection data 72 by a second sensor 26 that detects a detection object that exists in a second detection range 76 different from the first detection range 75 around the moving body 10. The method includes a step S12 of acquiring an environmental map 41 and a second environmental map 42 for the second sensor 26 that is created in advance for the travel area 60 of the mobile object 10, a step S13 of creating an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on at least one of the first detection data 71 and the first detection range 75 of the first sensor 25, and a step S14 of estimating the position of the mobile object 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44. According to the present disclosure, even if there are features that can be matched with the environmental map in a location that cannot be detected by the first sensor 25, such as outside the first detection range 75 or in a location that is a blind spot for the first sensor 25 due to the presence of an obstacle, the second detection data 72 of the second sensor 26 can be used to match the integrated map 44. As a result, the accuracy of estimating the position of the mobile object 10 is improved.

[0080] A position estimation method for a moving body according to a second aspect of the present disclosure is the position estimation method for a moving body according to the first aspect, and in step S13 of creating the integrated map 44, the integrated map 44 is created by using the first environmental map 41 for areas of the traveling area 60 of the moving body 10 that are included in the first detection range 75, and using the second environmental map 42 for areas that are not included in the first detection range 75. According to the present disclosure, an integrated map 44 is obtained in which the areas that are not included in the first detection range 75 are supplemented with the second environmental map 42, thereby reducing blind spots in the detection data in position estimation. As a result, the accuracy of position estimation is improved.

[0081] A moving body position estimation method according to a third aspect of the present disclosure is the moving body position estimation method according to the first or second aspect, and in step S13 of creating an integrated map 44, a predicted position of the moving body 10 at the time of acquisition of the first detection data 71 and the second detection data 72 is obtained based on the estimated position of the moving body 10 estimated in the past and movement data of the moving body 10, and an area included in the first detection range 75 at the predicted position is obtained. According to the present disclosure, an area in which the first environmental map 41 and an area in which the second environmental map 42 are adopted in the integrated map 44 can be appropriately determined from the predicted position of the moving body 10.

[0082] A moving body position estimation method according to a fourth aspect of the present disclosure is the moving body position estimation method according to any one of the first to third aspects, wherein in step S13 of creating the integrated map 44, the integrated map 44 is created such that the first environmental map 41 is adopted for areas where the degree of match between the first detection data 71 and the first environmental map 41 is within an acceptable range, and the second environmental map 42 is adopted for areas NA where the degree of match between the first detection data 71 and the first environmental map 41 is outside the acceptable range. According to the present disclosure, even within the first detection range 75, for areas NA where the first detection data 71 and the first environmental map 41 do not match due to, for example, the presence of an obstacle, the second environmental map 42 is adopted in the integrated map 44 and can be compared with the second detection data 72. As a result, the possibility of failure in estimating the position of the moving body 10 due to factors such as the presence of an obstacle can be reduced.

[0083] A position estimation method for a moving body according to a fifth aspect of the present disclosure is the position estimation method for a moving body according to any one of the first to fourth aspects, further comprising step S15 of updating a first environmental map 41 based on a plurality of first detection data 71 acquired at different positions, and updating a second environmental map 42 based on a plurality of second detection data 72 acquired at different positions. According to the present disclosure, when the detection data of the sensor and the environmental map do not match due to, for example, the presence of an obstacle, the environmental map can be updated using detection data acquired during the movement of the moving body, thereby reflecting the presence of the obstacle in the environmental map. Using the updated environmental map reduces the possibility of failure in position estimation of the moving body 10 due to factors such as the presence of an obstacle.

[0084] A mobile object position estimation method according to a sixth aspect of the present disclosure is the mobile object position estimation method according to any one of the first to fifth aspects, in which the first sensor 25 is disposed at a first height position H1 of the mobile object 10, the first environmental map 41 defines the shape of a travel area 60 within a plane including the first height position H1 of the mobile object 10, the second sensor 26 is disposed at a second height position of the mobile object 10 different from the first height position H1, and the second environmental map 42 defines the shape of the travel area 60 within a plane including the second height position H2 of the mobile object 10. According to the present disclosure, because the first sensor 25 and the second sensor 26 are disposed at different height positions, even in a situation where one of the first sensor 25 and the second sensor 26 is blocked by an obstacle or the like, the other may obtain detection data that can be compared with the environmental map. This reduces the possibility of failure in estimating the position of the mobile object 10 due to factors such as the presence of an obstacle.

[0085] A moving body position estimation method according to a seventh aspect of the present disclosure is the moving body position estimation method according to any one of the first to sixth aspects, wherein the first detection range 75 is a range of a predetermined angle (scanning angle θ1) centered on the moving body 10, and the second detection range 76 includes an angle range centered on the moving body 10 that is not included in the first detection range 75. According to the present disclosure, the blind spot of the first sensor 25 can be complemented by the second detection range 76 of the second sensor 26. This reduces the blind spot and makes it possible to more reliably detect a detection target that can be matched with an environmental map, thereby reducing the possibility of failure in estimating the position of the moving body 10.

[0086] The mobile body according to the eighth aspect of the present disclosure is an automatically moving mobile body 10, and is equipped with a first sensor 25 that detects detection objects present in a first detection range 75 around the mobile body 10, a second sensor 26 that detects detection objects present in a second detection range 76 different from the first detection range 75 around the mobile body 10, a memory unit 32 that stores a first environmental map 41 for the first sensor 25 that is created in advance for the traveling area 60 of the mobile body 10, and a second environmental map 42 for the second sensor 26 that is created in advance for the traveling area 60 of the mobile body 10, an integration processing unit 54 that creates an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on at least one of the first detection data 71 of the first sensor 25 and the first detection range 75, and an estimation processing unit 56 that performs processing to estimate the position of the mobile body 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44. According to the present disclosure, even if there are features that can be matched with an environmental map in a location that cannot be detected by the first sensor 25, such as a location outside the first detection range 75 or a location that is in a blind spot of the first sensor 25 due to the presence of an obstacle, the second detection data 72 of the second sensor 26 can be used to match the integrated map 44. As a result, the accuracy of position estimation of the moving body 10 is improved.

[0087] A program according to a ninth aspect of the present disclosure is a program 46 for causing a computer to execute a position estimation method for a moving body 10 that moves automatically, the program 46 including a step S10 of acquiring first detection data 71 by a first sensor 25 that detects a detection object that exists in a first detection range 75 around the moving body 10, a step S11 of acquiring second detection data 72 by a second sensor 26 that detects a detection object that exists in a second detection range 76 different from the first detection range 75 around the moving body 10, and a step S12 of acquiring a first environment for the first sensor 25 that is created in advance for a travel area 60 of the moving body 10. The method causes a computer (control device 28) to execute the following steps: step S12: acquiring a map 41 and a second environmental map 42 for the second sensor 26 that is created in advance for the travel area 60 of the mobile object 10; step S13: creating an integrated map 44 by integrating the first environmental map 41 and the second environmental map 42 based on at least one of the first detection data 71 and the first detection range 75 of the first sensor 25; and step S14: estimating the position of the mobile object 10 by comparing the first detection data 71 of the first sensor 25 and the second detection data 72 of the second sensor 26 with the integrated map 44. According to the present disclosure, even if there are features that can be matched with an environmental map in a location that cannot be detected by the first sensor 25, such as outside the first detection range 75 or in a location that is a blind spot for the first sensor 25 due to the presence of an obstacle, the second detection data 72 of the second sensor 26 can be used to match the integrated map 44. As a result, the accuracy of estimating the position of the mobile object 10 is improved.

[0088] Although the embodiments of the present disclosure have been described above, the embodiments are not limited to the contents of these embodiments. Furthermore, the above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments. [Explanation of symbols]

[0089] 10 Mobile 25 First Sensor 26, 26R, 26F Second sensor 32 Storage section 41 First Environmental Map 42 Second Environmental Map 44 Integrated Map 46 Programs 54 Integrated processing section 56 Estimation processing unit 60 Driving Area 71 First detection data 72, 72F, 72R Second detection data 75 First detection range 76, 76R, 76F Second detection range H1 First height position H2 Second height position

Claims

1. A method for estimating the position of an automatically moving object, comprising: acquiring first detection data by a first sensor that detects a detection target that exists in a first detection range around the moving object; acquiring second detection data by a second sensor that detects a detection target that exists in a second detection range around the moving object that is different from the first detection range; acquiring a first environmental map for the first sensor that is created in advance for a travel area of ​​the mobile object, and a second environmental map for the second sensor that is created in advance for the travel area of ​​the mobile object; creating an integrated map by integrating the first environmental map and the second environmental map based on at least one of the first detection data and the first detection range of the first sensor; estimating a position of the moving object by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map; Equipped with A method for estimating the position of a moving object.

2. In the step of creating the integrated map, the integrated map is created so that the first environmental map is used for an area of ​​the traveling area of ​​the mobile object that is included in the first detection range, and the second environmental map is used for an area that is not included in the first detection range. The method for estimating the position of a moving object according to claim 1 .

3. In the step of creating the integrated map, a predicted position of the moving body at a time when the first detection data and the second detection data were acquired is obtained based on an estimated position of the moving body estimated in the past and movement data of the moving body, and an area included in the first detection range at the predicted position is obtained. The method for estimating the position of a moving object according to claim 2 .

4. In the step of creating the integrated map, the integrated map is created so that the first environmental map is adopted for an area where the degree of coincidence between the first detection data and the first environmental map is within an acceptable range, and the second environmental map is adopted for an area where the degree of coincidence between the first detection data and the first environmental map is outside the acceptable range. The method for estimating the position of a moving object according to claim 1 .

5. updating the first environmental map based on a plurality of the first detection data acquired at different positions, and updating the second environmental map based on a plurality of the second detection data acquired at different positions, The method for estimating the position of a moving object according to claim 4.

6. the first sensor is disposed at a first height position of the moving body, the first environmental map defines a shape of the traveling area within a plane including the first height position of the moving object; the second sensor is disposed at a second height position of the moving body that is different from the first height position, the second environmental map defines the shape of the traveling area within a plane including the second height position of the moving object; The method for estimating the position of a moving object according to any one of claims 1 to 5.

7. the first detection range is a range of a predetermined angle centered on the moving object, the second detection range includes an angle range that is not included in the first detection range and is centered on the moving object; The method for estimating the position of a moving object according to any one of claims 1 to 5.

8. A mobile object that moves automatically, a first sensor that detects a detection target that exists in a first detection range around the moving object; a second sensor that detects a detection target that exists in a second detection range around the moving object that is different from the first detection range; a storage unit that stores a first environmental map for the first sensor that is created in advance for a travel area of ​​the moving object, and a second environmental map for the second sensor that is created in advance for the travel area of ​​the moving object; an integration processing unit that creates an integrated map by integrating the first environmental map and the second environmental map based on at least one of first detection data of the first sensor and the first detection range; an estimation processing unit that performs processing to estimate a position of the moving object by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map; Equipped with Mobile object.

9. A program for causing a computer to execute a method for estimating the position of an automatically moving object, acquiring first detection data by a first sensor that detects a detection target that exists in a first detection range around the moving object; acquiring second detection data by a second sensor that detects a detection target that exists in a second detection range around the moving object that is different from the first detection range; acquiring a first environmental map for the first sensor that is created in advance for a travel area of ​​the mobile object, and a second environmental map for the second sensor that is created in advance for the travel area of ​​the mobile object; creating an integrated map by integrating the first environmental map and the second environmental map based on at least one of the first detection data and the first detection range of the first sensor; estimating a position of the moving object by comparing the first detection data of the first sensor and the second detection data of the second sensor with the integrated map; The computer executes the program.

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