WO2024034025A1

WO2024034025A1 - Autonomous movement control device, autonomous movement system, autonomous movement method, and program

Info

Publication number: WO2024034025A1
Application number: PCT/JP2022/030490
Authority: WO
Inventors: 誠史吉田
Original assignee: 日本電信電話株式会社
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2024-02-15

Abstract

The present disclosure has a purpose of reducing a processing load and enable autonomous movement such as autonomous travel and the like.　For that purpose, the present disclosure provides an autonomous movement control device that controls a movement of an autonomous moving body that is capable of moving autonomously, the autonomous movement control device comprising: a self-position estimation unit that estimates, by pattern matching data that relates to an image that is obtained by imaging quasi-periodic tiles that are composed of a plurality of types of tile shapes and data that has a coordinate system that corresponds to the quasi-periodic tiles, a self-position and a traveling direction of the autonomous moving body on the quasi-periodic tiles; and a control unit that controls a movement of the autonomous moving body, on the basis of the self-position and the traveling direction of the autonomous moving body that were estimated by the self-position estimation unit, and a movement route of the autonomous moving body that is set in advance.

Description

Autonomous movement control device, autonomous movement system, autonomous movement control method, and program

The present disclosure relates to a technology for controlling a mobile body capable of autonomous movement.

AGVs (Automatic Guided Vehicles) are used in indoor environments such as factories and warehouses, which autonomously travel along routes without requiring human operation. When an automated guided vehicle (vehicle) runs, it is necessary to estimate the vehicle's own position and control the vehicle so that it travels along a predetermined route. Control methods for driving automatic guided vehicles include magnetic guidance, optical guidance, image recognition, laser guidance, and SLAM (Simultaneous Localization and Mapping).

The magnetic induction method is a method in which a magnetic sensor mounted on the vehicle guides the vehicle along the driving route by detecting the magnetic field emitted by magnetic bars or magnetic tape laid on the floor along the route. The magnetic induction method has the advantage of being simple and highly reliable, but has the disadvantage of being costly when changing routes. In addition, there is a possibility that it will be affected by magnetic fields due to piping and the like existing near the route.

The optical guidance method is a method in which an optical sensor mounted on the vehicle guides the vehicle along the driving route by detecting the route formed on the floor by laying guidance tape. The optical induction method has the advantage of being cheaper than the magnetic induction method.

The image recognition method uses a camera mounted on the vehicle to read markers such as QR codes (registered trademarks) installed on the floor or ceiling, allowing the vehicle to determine its own location and automatically drive to its destination. be. Note that these three control methods are methods that allow the vehicle to travel only on a pre-fixed route, and are also called route guidance methods.

In the laser guidance method (reflector guidance method), a laser beam irradiation device mounted on a vehicle irradiates a laser beam onto a reflective plate installed on a wall, floor, or pillar, and a receiver mounted on the vehicle This is a method in which a vehicle estimates its own position by detecting reflected waves from a reflector and travels without using guides.

SLAM guidance methods include the Visual SLAM method that uses a camera and the LiDAR SLAM method that uses LiDAR (Light Detection and Ranging). Visual SLAM is a method in which a camera mounted on a vehicle acquires continuous changes in image data of the surrounding environment, such as walls, to estimate its own position and drive without using guides. In the LiDAR SLAM method, a laser light irradiation device mounted on a vehicle irradiates laser light onto the surrounding environment such as a wall, and a laser light receiving device mounted on the vehicle takes the time it takes for reflected light from the surrounding environment to be returned. In this method, the vehicle estimates its own position by measuring the distance and direction to the surrounding environment and runs without using guides. The laser guidance method and SLAM method are also called autonomous mobile methods because they do not require a guide.

Additionally, AGVs that use SLAM are also called AMRs (Autonomous Mobile Robots). The advantage of the SLAM method is that there is no need to install guides, so you can freely set the travel route. In addition, with the route guidance method, the vehicle temporarily stops when it detects an obstacle, but with the SLAM method, the vehicle can avoid the obstacle and continue driving by resetting the route.

On the other hand, the SLAM method requires processing to eliminate the effects of changes in the surrounding environment, such as opening and closing of doors, changes in the loading status of goods on shelves, and movements of workers and other transport vehicles. In addition, there is a problem that movement may be hindered in a space without feature points.

As an autonomous movement system that addresses these issues with the SLAM system, a system that extracts feature points from floor image data captured by a camera mounted on a vehicle and traces them to automatically drive is being considered ( Non-patent document 1).

However, methods for extracting feature points from image data of the surrounding environment include a method that extracts feature points from the image (feature based method) and a method that refers to the entire image (direct method). ), but in either case, if there are various changes in the surrounding environment due to pedestrians other than static objects, moving vehicles, opening/closing of doors, etc. on the driving route, map data is generated and images are generated. In the process of performing the matching process, image processing becomes complicated and the amount of processing increases, resulting in an increase in processing load.

The present invention was made in view of the above-mentioned circumstances, and an object of the present invention is to suppress the processing load and enable autonomous movement such as autonomous driving.

In order to achieve the above object, the invention according to claim 1 provides an autonomous movement control device for controlling the movement of an autonomous mobile body capable of autonomously moving, wherein quasi-periodic tiles constituted by a plurality of types of tile shapes are provided. The self-position and traveling direction of the autonomous mobile body in the quasi-periodic tile can be determined by pattern matching data regarding the image obtained by photographing and map data having a coordinate system corresponding to the quasi-periodic tile. Control of movement of the autonomous mobile body based on a self-position estimating unit to estimate, the self-position and the traveling direction estimated by the self-position estimation unit, and a preset travel route of the autonomous mobile body. This is an autonomous movement control device having a control unit that performs the following.

As explained above, according to the present invention, it is possible to achieve autonomous movement with a reduced processing load.

1 is an overall configuration diagram of an autonomous driving system according to an embodiment. FIG. 1 is a configuration diagram of an automatic driving vehicle. FIG. 2 is an electrical hardware configuration diagram of the information processing device. FIG. 3 is a plan view of a quasi-periodic tile. It is a figure showing the shape of each tile. FIG. 3 is a plan view showing a travel pattern of initialization travel. It is a flowchart which shows the process of initialization driving|running|working. FIG. 3 is a plan view showing a driving route for the actual driving. It is a flowchart which shows the process of a main run. FIG. 4 is a diagram clarifying the 12-fold symmetry part in the quasi-periodic tile.

Hereinafter, embodiments of the present invention will be described using the drawings.

[System configuration of embodiment]
An outline of the configuration of an autonomous driving system according to an embodiment will be described using FIG. 1. FIG. 1 is an overall configuration diagram of an autonomous driving system according to an embodiment.

As shown in FIG. 1, an autonomous driving system 1 is constructed of an autonomous driving vehicle (also referred to as a "vehicle") 2 such as an automatic guided vehicle capable of autonomous driving, and quasi-periodic tiles 8 on the floor. . The vehicle 2 estimates its own position and traveling direction and moves by reading a pattern formed by a plurality of tile shapes of the quasi-periodic tiles 8.

[Explanation of quasi-periodic tiles]
As shown in FIG. 4A, the quasi-periodic tile 8 is a tile that is formed of a plurality of types of tile shapes and has a non-periodic geometric pattern drawn thereon, and is installed on the floor surface. As shown in FIG. 4B, the plurality of types of tile shapes are composed of three types of figures (a square with equal side length L and two types of diamonds). The quasi-periodic tile 8 realizes a planar filling with no periodic pattern (no overlapping period due to parallel movement) using finite types of polygons, unlike a planar filling with a periodic pattern made up of regular polygons. This is a tiling method. As an example, Penrose tiling is known, which realizes flat filling using two types of rhombuses. The geometric pattern of the quasi-periodic tiles 8 shown in FIGS. 4A and 4B is just an example, and may be a geometric pattern without periodicity.

In this embodiment, the aperiodic nature of the pattern of the quasi-periodic tiles 8 is utilized for estimating the self-position of the vehicle 2. The quasi-periodic tiles 8 are constructed on the floor surface, and are imaged by an imaging unit 20 (described later) mounted on the vehicle 2. Note that as long as the straight lines constituting the quasi-periodic tiles 8 can be optically identified by the imaging unit 20, the tile pattern may be drawn with paint or the like on the floor surface on which the vehicle 2 runs, or the sheet on which the tile pattern is printed may be used as a carpet. May be used as Furthermore, the pattern of the quasi-periodic tiles 8 may be constructed by pasting tape or the like on the floor surface, or the pattern of the quasi-periodic tiles 8 may be constructed by laying tiles of a plurality of shapes on the floor surface. Alternatively, the pattern of the quasi-periodic tiles 8 may be displayed on a display element such as a display installed on the floor, or the pattern of the quasi-periodic tiles 8 may be projected onto the floor. In either case, it is assumed that the tiles are configured with sufficient density to specify the position of the vehicle 2. In other words, quasi-periodic tiles are configured with the density (size) necessary to estimate the self-position of the vehicle 2 based on the number of pixels of the imaging unit 20 mounted on the vehicle 2 and the distance from the floor surface. shall be.

Note that the pattern of the quasi-periodic tiles 8 does not necessarily need to be visible, and may be constructed using, for example, an invisible marker that emits light in response to light of a wavelength outside visible light. In that case, the imaging unit 20 is configured with an element that detects the light beam of the invisible marker.

[Configuration of autonomous vehicle]
Next, the configuration of the vehicle 2 will be explained using FIG. 2. FIG. 2 is a configuration diagram of the autonomous vehicle 2. As shown in FIG.

The vehicle 2 is configured by an autonomous driving control device 3 and a traveling device 6. The autonomous driving control device 3 photographs the quasi-periodic tiles 8 to estimate the self-position and traveling direction of the vehicle 2, and controls the operation of the traveling device 6. The traveling device 6 is a device that is configured with a motor, tires, a battery, a steering mechanism, etc., and causes the vehicle 2 to travel.

<Autonomous driving control device>
The autonomous running control device 3 includes an information processing device 5, an imaging unit 20, a gyro sensor 26, an acceleration sensor 27, and an odometry 28.

The photographing unit 20 is a camera that photographs the floor surface of the quasi-periodic tiles 8 and the like to generate (obtain) image data. The photographing unit 20 is fixedly installed in the vehicle 2 facing toward the floor surface, and photographs the floor surface. It is assumed that the offset (relative position) between any pixel on the image data obtained by the imaging unit 20 and the reference position of the vehicle 2 has been measured and calibrated (calibration, proofreading, adjustment) in advance. Note that the photographing unit 20 may be a camera that performs monocular photography, a camera that performs stereo photography, or an omnidirectional imaging camera equipped with a fisheye lens. Furthermore, the imaging unit 20 may have one camera, or a plurality of cameras may share the imaging range. Furthermore, the imaging unit 20 may be a remote sensing device such as LiDAR other than a camera.

The information processing device 5 is a computer such as a PC (Personal Computer), and based on the position and orientation of the photographing section 20 installed in the vehicle 2 and the data regarding the image acquired from the photographing section 20, the information processing device 5 determines the characteristics on the image. By measuring the relative position of the vehicle 2 with respect to points (sides and intersections of the quasi-periodic tiles 8), the self-position (absolute position) and traveling direction of the vehicle 2 can be estimated, and the operation of the traveling device 6 can be controlled. A control signal is sent to the traveling device 6. The information processing device 5 will be explained in detail later.

If the autonomous driving control device 3 of this embodiment has the imaging unit 20 and the information processing device 5, autonomous driving is possible by reading the quasi-periodic tiles 8, but due to a failure of the imaging unit 20, etc. Even if it becomes impossible to obtain image-related data, it is possible to continue autonomous driving through dead reckoning using sensors, etc. Furthermore, the sensors and the like provide robustness to the local periodicity and rotational symmetry of the pattern of the quasi-periodic tiles 8 in the self-position and orientation estimation operation of the vehicle 2 during autonomous travel. However, each parameter of the gyro sensor 26, the acceleration sensor 27, and the odometry 28 needs to be calibrated at the stage of initialization driving (FIG. 5), which will be described later, before a failure of the photographing unit 20 occurs.

Here, the gyro sensor 26 is a sensor that detects the angle, angular velocity, or angular acceleration of an object (here, the vehicle 2). The acceleration sensor 27 is a sensor that measures the acceleration of an object (here, the vehicle 2). The odometry 28 is a device that measures the number of rotations of the wheels of the vehicle 2 using a rotary encoder, for example, and calculates the moving distance, speed, and turning angle of the vehicle 2 from the number of rotations of the wheels. The odometry 28 is a device for further increasing the accuracy of self-position estimation, and is a device that complements the gyro sensor 26 and the acceleration sensor 27, so it does not necessarily need to be provided in the autonomous travel control device 3. That is, the odometry 28 allows the vehicle 2 to improve the estimation accuracy and reliability of its own position.

<Information processing device>
Next, the information processing device 5 will be explained in detail.

(Hardware configuration of information processing device)
The hardware configuration of the information processing device will be explained using FIG. 3. FIG. 3 is an electrical hardware configuration diagram of the information processing device 5. As shown in FIG.

As shown in FIG. 3, the information processing device 5 is a computer that includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, and an SSD (Solid State Drive) 504. , an external device connection I/F (Interface) 505, a network I/F 506, a media I/F 509, and a bus line 510.

Among these, the CPU 501 controls the operation of the entire information processing device 5. The ROM 502 stores programs used to drive the CPU 501, such as IPL (Initial Program Loader). RAM 503 is used as a work area for CPU 501.

The SSD 504 reads or writes various data under the control of the CPU 501. Note that an HDD (Hard Disk Drive) may be used instead of the SSD 504.

The external device connection I/F 505 is an interface for connecting various external devices. External devices in this case include a display, speaker, keyboard, mouse, USB (Universal Serial Bus) memory, printer, and the like.

The network I/F 506 is an interface for data communication via a communication network such as the Internet and/or LAN (Local Area Network). Note that a wireless communication device may be used instead of the network I/F 506.

The media I/F 509 controls reading or writing (storage) of data to a recording medium 509m such as a flash memory. The recording media 509m also include DVDs (Digital Versatile Discs), Blu-ray Discs (registered trademark), and the like.

The bus line 510 is an address bus, a data bus, etc. for electrically connecting each component such as the CPU 501 shown in FIG. 3.

(Functional configuration of information processing device)
Next, returning to FIG. 2, the functional configuration of the information processing device 5 will be described.

The information processing device 5 includes a route data setting section 31, a self-position estimation section 33, and a control section 35. Each of these units is a function realized by instructions from the CPU 501 according to a program stored in the RAM 503 or the like. Further, a route data storage section 21, an image data storage section 22, and a map data storage section 23 are constructed in the RAM 503 or SSD 504 of the information processing device 5.

Among these, the route data storage section 21 stores data of the travel route set by the route data setting section 31. The route data is given, for example, as a set of coordinate values of a starting point, an ending point, and a waypoint on the floor.

The image data storage unit 22 stores data regarding each image obtained by being continuously captured by the imaging unit 20 while the vehicle 2 is running. The image-related data stored in the image data storage unit 22 may be the image data itself, or may be data with a smaller data capacity (for example, polygon data that extracts the shape of a tile from the image data or identified finite types). data recording the arrangement of tiles).

The map data storage unit 23 stores map data (information) with coordinate data of drawing data of the floor surface forming the quasi-periodic tile 8, and is referred to during processing in the self-position estimating unit 33.

The route data setting unit 31 accepts the setting of the travel route of the vehicle 2 from the user, and stores the data of the set travel route in the route data storage unit 21.

The self-position estimating unit 33 performs a matching process (pattern matching) between the image-related data stored in the image data storage unit 22 and the map data stored in the map data storage unit 23, thereby determining the vehicle's self-position. (current position) and advancing direction (movement direction). Note that when an emergency situation occurs due to a failure of the photographing unit 20 or the like, as described above, the self-position estimating unit 33 uses at least the gyro sensor 26 and the acceleration sensor of the gyro sensor 26, the acceleration sensor 27, and the odometry 28. An output signal is obtained from the sensor 27 and used as data on the traveling direction and speed of the vehicle 2 in an emergency.

The control unit 35 controls the traveling device 6 based on the data acquired from the self-position estimating unit 33 (estimated values of the self-position and traveling direction of the vehicle 2) and the travel route data acquired from the route data storage unit 21. A control signal is generated and sent to the traveling device 6. Note that the vehicle 2 may be equipped with a photographing unit (the photographing unit 20 or another photographing unit), LiDAR, RADAR, etc. for detecting obstacles in the traveling direction of the vehicle 2. When an obstacle is detected, the route data setting section 31 sets a re-route to avoid the obstacle in real time, and stores the re-route data in the route data storage section 21.

Note that all or part of the functions of the information processing device 5 may be installed at a location other than the autonomous vehicle 2, for example, on a cloud platform via a communication network.

[Processing or operation of autonomous vehicle]
Next, the processing or operation of the vehicle 2 will be explained using FIGS. 5 to 8. FIG. 5 is a plan view showing a travel pattern for initialization travel. FIG. 6 is a flowchart showing the initialization traveling process. FIG. 7 is a plan view showing the route of the main run. FIG. 8 is a flowchart showing the processing of the main run.

The map data storage unit 23 stores map data having a coordinate system corresponding to a plan view of a floor surface having a pattern of quasi-periodic tiles 8. The imaging unit 20 is fixed to the vehicle 2 and continuously images the floor surface near the vehicle 2 while the vehicle 2 is running. Unlike the conventional technology, the self-position estimating unit 33 of the vehicle 2 does not extract feature points from the entire image data of the captured floor surface near the vehicle 2, but extracts feature points from the image data of the floor surface by straight lines and intersection points. Image-related data stored in the image data storage unit 22 is used to extract each shape of the quasi-periodic tiles 8 and identify which of the three types of tile shapes it corresponds to by pattern matching. use.

First, before the main run of the vehicle 2, the vehicle 2 performs initialization (preparation) run in order to identify its own position and orientation. For example, the user uses the route data setting section 31 to set a driving pattern for initializing driving. For example, as shown in FIG. 5, the initial driving pattern is such that the vehicle 2 travels straight from the initial state, detects the boundary (end) of the quasi-periodic tile 8, and then moves clockwise along the boundary. A running pattern is used. Alternatively, a traveling pattern may be used in which the vehicle moves randomly on the quasi-periodic tiles 8 during the initial traveling. The data of the set driving pattern for the initialized driving is stored in the route data storage section 21.

Further, for the main run of the vehicle 2, for example, the user uses the route data setting unit 31 to set a travel route (route) on the floor surface on which the quasi-periodic tiles 8 are constructed, as indicated by arrows in FIG. 7 for the main run. Set the route. The data of the set route for the actual driving is stored in the route data storage section 21.

After such preparations are completed, the following processing is executed.

<Initialization running process or operation>
First, the vehicle 2 performs an initialization run as shown below using the data of the initialization run pattern with the user placing the vehicle 2 on the quasi-periodic tile 8 in an arbitrary position and orientation as the initial state. Perform processing or operation (Figure 6). Note that, as described above, when the vehicle 2 detects an obstacle, the vehicle 2 may avoid the obstacle and construct a new route.

S11: As the vehicle 2 travels, the photographing section 20 stores data regarding an image obtained by photographing the pattern of the quasi-periodic tiles 8 in the image data storage section 22. Thereby, the vehicle 2 can store in chronological order the state of change in the pattern formed by the three types of tiles on the image as the vehicle 2 travels.

S12: The self-position estimating unit 33 extracts the pattern of the quasi-periodic tiles 8 from the data related to the image read from the image data storage unit 22.

S13: The self-position estimating unit 33 compares the extracted pattern with the map data pattern stored in the map data storage unit 23 (pattern matching) to determine the self-position (absolute position) and progress of the vehicle 2. Estimate direction. The quasi-periodic tile 8 has no periodicity in its pattern, but locally has periodicity and rotational symmetry. Therefore, by driving the vehicle 2 over a sufficient range during initialization, the positional ambiguity can be resolved and the self-position of the vehicle 2 can be specified as an absolute position (coordinates) on the map. Moreover, once the position on the map can be specified, the vehicle 2 can continuously estimate its own position and traveling direction by continuously photographing and tracking the tile pattern while driving.

S14: The self-position estimating unit 33 continuously photographs and tracks the pattern of the quasi-periodic tiles 8 to continuously estimate the self-position and the direction of travel. Calibrate (calibrate, calibrate, adjust) each parameter. Note that each parameter may be held by the gyro sensor 26, the acceleration sensor 27, and the odometry 28, respectively, or may be stored separately by a parameter storage unit constructed from the RAM 503 or the like. Calibration of the gyro sensor 26 and the acceleration sensor 27 includes initialization of parameters in an initial (stationary) state and optimization of parameters such as an extended Kalman filter in a moving state. Calibration of the odometry 28 includes optimization of coefficients when calculating the speed of the vehicle 2 from the rotation speed of the wheels of the vehicle 2 obtained by the encoder.

S15: The self-position estimating unit 33 determines whether the initialization run has been completed based on the estimated state of the self-position and traveling direction of the vehicle 2, and the state of calibration. If the initialization run has not been completed, the process returns to step S11. On the other hand, if the initialization run is completed, the process proceeds to S21 below. Note that the calibration of each parameter may be performed after the initialization run is completed.

Note that in S11 to S13, as an example of handling image-related data rather than image data itself, we focus on the intersections of each tile in the quasi-periodic tiles 8 on the image data (represented by the image data), and construct the intersections. By referring to the map data using the number of line segments and the pattern of angles formed by these line segments as clues (pattern matching) and specifying which intersection point in the quasi-periodic tile 8, the vehicle 2 in the quasi-periodic tile 8 is There is a method for estimating the self-position and direction of travel. Point A in Figure 5 is the intersection of nine line segments, and the angles formed by each line segment at point A are a: 30°, b: 60°, c: 90°, d: 120°, and e: 150°. baaabbaaa clockwise. The intersection point that is cyclically arranged at the same angle on the quasi-periodic tile 8 is point C, but by detecting point B following point A in the direction of travel of the vehicle 2, it can be specified that the intersection point is point A. Can be done. By performing the initialization run on the quasi-periodic tile 8 with the vicinity of the characteristic intersection as the initial position, the time required to complete the initialization run can be shortened.

With the above steps, the initialization running process is completed.

<Processing or operation of main run>
Subsequently, the vehicle 2 uses the route data for the actual trip (FIG. 7) to perform the following processing or operation for the actual trip (FIG. 8). Note that, as described above, when the vehicle 2 detects an obstacle, the vehicle 2 may avoid the obstacle and construct a new route.

S21: As the vehicle 2 travels, the photographing section 20 stores data regarding an image obtained by photographing the pattern of the quasi-periodic tiles 8 in the image data storage section 22. Thereby, the vehicle 2 can store in chronological order the state of change in the pattern formed by the three types of tiles on the image as the vehicle 2 travels.

S22: The self-position estimating unit 33 extracts a pattern from the data related to the image read from the image data storage unit 22.

S23: The self-position estimating unit 33 compares (or collates) the extracted pattern with the map pattern stored in the map data storage unit 23 to determine the self-position (absolute position) and traveling direction of the vehicle 2. Estimate.

S24: The control unit 35 compares the self-position and traveling direction of the vehicle 2 with the route data stored in the route data storage unit 21, generates a control signal, and outputs this control signal to the traveling device 6. Note that even during the actual running, the self-position estimating unit 33 may calibrate each parameter of the gyro sensor 26, acceleration sensor 27, and odometry 28 from the estimated self-position and orientation at any time.

S25: The self-position estimating unit 33 determines whether the end point has been reached based on the route data for the actual run. If the end point has not been reached, the process returns to step S21. On the other hand, when the end point has been reached, the processing of the main run ends.

With the above, the processing of the main run ends.

Note that the initialization run does not necessarily need to be performed before the main run. After the main run, if the vehicle 2 is stopped at an arbitrary parking position on the quasi-periodic tile 8 and the data of the position and orientation of the vehicle 2 are memorized, the main run can be started immediately without having to perform initialization run at startup. You can move to the starting point and start the actual run.

As described above, the vehicle 2 of this embodiment extracts a combination pattern of simple-shaped tiles from image data, performs pattern matching with the stored data of the quasi-periodic tiles 8, and obtains absolute coordinates from the tile positions. By doing so, it is possible to estimate the self-position with high accuracy while suppressing the processing load. At this time, since the vehicle 2 can trace the relative displacement, that is, the moving direction and moving distance with high accuracy even when moving in any direction, by the tile shape, the vehicle 2 can continuously estimate its own position and traveling direction. By controlling the vehicle with reference to route data, it is possible to accurately travel along a virtual route. It can also create routes to avoid obstacles in real time and avoid them accurately.

Note that the quasi-periodic tile 8 locally has two-dimensional rotational symmetry. For example, as shown in FIG. 9, in the quasi-periodic tile 8, the part within the broken line has 12-fold symmetry, so if the area near the center of rotational symmetry in the radial direction is included in the movement route of the vehicle 2, , it is necessary to consider the ambiguity of orientation. This ambiguity can be solved by using orientation data from the gyro sensor 26.

[Application example of this embodiment]
As in this embodiment, when the vehicle 2 moves while reading the quasi-periodic tiles 8, when used in an outdoor environment, the system is used in areas where GNSS (Global Navigation Satellite Systems) signals cannot be received, such as under elevated tracks. or for estimating absolute position in an urban canyon reception environment. Specifically, in areas where it is difficult to receive GNSS signals, when performing composite positioning in which relative positioning means such as the imaging unit 20, LiDAR, Radar, etc. are fused to GNSS positioning, in order to eliminate the cumulative error of the relative positioning means. A pattern of quasi-periodic tiles 8 is constructed on a part of the road (for example, at an intersection), and when the vehicle 2 passes by, the photographing unit 20 images the tile pattern of the quasi-periodic tiles 8 to determine the absolute position of the vehicle 2. Possible uses include estimating .

[Effects of embodiment]
As explained above, according to the present embodiment, even when there are various changes in the surrounding environment such as pedestrians, moving vehicles, opening and closing of doors, etc. on the travel route of the vehicle 2, the vehicle 2 can By moving while reading the pattern of the quasi-periodic tiles 8, it is possible to estimate the self-position with high accuracy while suppressing the processing load, and to autonomously travel to the destination.

Furthermore, according to the present embodiment, it is possible to operate the vehicle 2 with flexible route setting without using a guide. Therefore, during autonomous driving of the vehicle 2, even if the vehicle 2 temporarily avoids an obstacle, it can quickly return to the route from any position.

Furthermore, unlike the Visual SLAM method, the method in which the vehicle 2 moves while reading the quasi-periodic tiles 8, as in this embodiment, does not require brightness to recognize the entire environment in which the vehicle 2 is traveling. It is sufficient if there is enough local brightness to distinguish the pattern on the floor near 2. Also, unlike the SLAM method, it is not affected by changes in the surrounding environment, so processing and operation are not affected by changes in the positions of other unmanned vehicles or workers. It can also be applied to indoor or outdoor environments with a large floor area and few feature points. Furthermore, it is possible to move objects (such as luggage) placed on the floor surface on which the vehicle 2 travels at any time, and to dynamically change the layout of the floor surface.

〔supplement〕
As described above, the present invention is not limited to the above-described embodiments, and various modifications and applications can be made, for example, as shown below.

(1) The information processing device 5 can be realized by a computer and a program, but this program can also be recorded on a (non-temporary) recording medium or provided via a communication network such as the Internet.

(2) The CPU 101 (microprocessor) may be one or more.

(3) The vehicle 2 of the above embodiment is an example of an autonomous mobile body that can move autonomously. Autonomous mobile objects also include ships, aircraft, and underwater vehicles (for example, a mobile object that detects cracks in a swimming pool). The autonomous travel control device 3 is also an example of an autonomous movement control device. That is, in this specification, the word "running" can be replaced with "moving". Further, the traveling device 6 may be moved by a jet engine, a propeller, or the like instead of tires.

1 Autonomous driving system (an example of an autonomous moving system)
2 Autonomous vehicle (an example of an autonomous mobile object)
3 Autonomous travel control device (an example of an autonomous movement control device)
5 Information processing device 6 Traveling device (an example of a moving device)
8 Quasi-periodic tiles 20 Photographing section 21 Route data storage section 22 Image data storage section 23 Map data storage section 26 Gyro sensor 27 Acceleration sensor 28 Odometry 31 Route data setting section 33 Self-position estimation section 35 Control section

Claims

An autonomous movement control device that controls the movement of an autonomous mobile body that can move autonomously,
By pattern matching data regarding images obtained by photographing quasi-periodic tiles composed of multiple types of tile shapes and map data having a coordinate system corresponding to the quasi-periodic tiles, the quasi-periodic a self-position estimation unit that estimates the self-position and traveling direction of the autonomous mobile body in the tile;
a control unit that controls movement of the autonomous mobile body based on the self-position and the traveling direction estimated by the self-position estimation unit, and a preset movement route of the autonomous mobile body;
An autonomous movement control device having:
The autonomous movement control device according to claim 1,
It has a gyro sensor that detects the angle, angular velocity, or angular acceleration of the autonomous moving body, and an acceleration sensor that measures the acceleration of the autonomous moving body,
The self-position estimation unit is an autonomous movement control device, wherein the self-position estimating unit calibrates each parameter of the gyro sensor and the acceleration sensor based on the estimated self-position and the traveling direction.
The self-position estimating unit calculates the self-position and the self-position of the autonomous mobile body using the calibrated gyro sensor and the acceleration sensor instead of the pattern matching when data related to the image cannot be acquired. The autonomous movement control device according to claim 2, which estimates the traveling direction.
The autonomous mobile body is an autonomous vehicle having wheels,
The autonomous movement control device has an odometry that calculates the travel distance, speed, and rotation angle from the rotation speed of the wheel,
The autonomous movement control device according to claim 2, wherein the self-position estimation unit calibrates the odometry parameters based on the estimated self-position and the traveling direction.
The self-position estimating unit performs pattern matching on the map data based on the number of line segments constituting the intersection of each tile in the quasi-periodic tiles displayed in the data related to the image and the pattern of angles formed by the line segments, The autonomous movement control device according to claim 1, wherein the self-position and the traveling direction of the autonomous mobile body in the quasi-periodic tile are estimated by specifying which intersection in the quasi-periodic tile.
The autonomous mobile body comprising the autonomous movement control device according to any one of claims 1 to 5,
the quasi-periodic tile;
An autonomous mobile system with
An autonomous movement control method executed by an autonomous movement control device that controls the movement of an autonomous mobile body that can move autonomously, the method comprising:
The autonomous movement control device includes:
By pattern matching data regarding images obtained by photographing quasi-periodic tiles composed of multiple types of tile shapes and map data having a coordinate system corresponding to the quasi-periodic tiles, the quasi-periodic a self-position estimation process for estimating the self-position and traveling direction of the autonomous mobile body in the tile;
a control process that controls movement of the autonomous mobile body based on the self-position and the traveling direction estimated by the self-position estimation process, and a preset movement route of the autonomous mobile body;
An autonomous movement control method that performs
A program that causes a computer to execute the method according to claim 7.