Control device, method, and program

Abstract

To provide a control device for an autonomous robot that estimates its self-location through collation with a map, which can simply stabilize the self-location estimation.SOLUTION: A control device is for an autonomous robot that is configured to estimate its self-location based at least on a first positioning result obtained by collating spatial data of a surrounding environment measured by the autonomous robot with given map information. If the accuracy of the first positioning result is determined to be low or if the self-location estimation result falls within a predetermined area, the control device replaces the spatial data of the surrounding environment measured by the autonomous robot with given data in areas that are predetermined to be expected to have reduced measurement accuracy, and then collates the data with the given map information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a control device, a method, and a program for controlling an autonomous mobile robot. [Background technology]

[0002] An example of a conventional technology for controlling an autonomous mobile robot is Patent Document 1, which discloses that environmental information includes the width of the path along which the autonomous robot travels, the condition of the road surface, and information about obstacles present on the path, and that speed and other parameters are set as setting parameters for autonomous travel in accordance with the environmental information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-120505 Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology of Patent Document 1, parameters are set appropriately on the assumption that self-location estimation can be stably and appropriately realized, and no consideration is given to dealing with situations in which self-location estimation cannot be appropriately realized.

[0005] Autonomous mobile robots estimate their own location by comparing map data composed of point clouds and other data with spatial data about their surroundings. Situations in which this self-location estimation cannot be performed properly include, for example, when there are plants along a path, and the numerous leaves of these plants are arranged in irregular orientations, causing various occlusions depending on the viewing direction, or when the leaves are shiny, which can cause noise in the spatial data measured by LiDAR (Light Detection and Ranging) or other methods, making it difficult to obtain a sufficiently dense point cloud. Figure 1 shows an example of such a situation, where the planted area only allows for sparse measurement of the point cloud.

[0006] In addition to situations where point clouds are only obtained sparsely or there is a lot of noise, such as in the case of the plants mentioned above, there may also be cases where the area in which an autonomous robot is moving is a vast open space, and there are no or few detectable objects to measure for self-localization, making it difficult for the autonomous robot to move. In such cases, it may be possible to use a global positioning system (GPS) in addition, but GPS may not be available indoors, and even if it is available, problems may arise such as the complexity of processing due to using a positioning method other than point cloud matching, or the increased cost of adding a positioning method.

[0007] In view of the problems of the conventional technology, the present invention aims to provide a control device for an autonomous mobile robot that estimates its own position by comparing it with a map, which can simply stabilize the self-position estimation, and also aims to provide a method and a program corresponding to the control device. [Means for solving the problem]

[0008] To achieve the above object, the present invention provides a control device for an autonomous mobile robot, wherein the autonomous mobile robot is configured to estimate its own position based at least on a first positioning result obtained by comparing spatial data of the surrounding environment measured by the autonomous mobile robot with given map information, and when it is determined that the accuracy of the first positioning result is low or when the result of the self-location estimation falls within a predetermined area, the control device controls the autonomous mobile robot to replace a portion of the spatial data of the surrounding environment measured by the autonomous mobile robot that is predetermined as a portion where measurement accuracy is expected to be reduced with given data, and then compare the replaced portion with the given map information. The present invention also provides a method and a program corresponding to the control device. [Effects of the Invention]

[0009] According to the present invention, in areas where the accuracy of self-location estimation is expected to decrease or where it actually decreases, the measured spatial data of the surrounding environment is replaced with given data and then compared with a map, thereby making it possible to simply stabilize self-location estimation. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 10 is a diagram showing an example of a problem where a point cloud can only be measured sparsely. [Figure 2] 1 is a configuration diagram of a map use system according to an embodiment. [Figure 3] FIG. 1 is a functional block diagram of a map use system according to an embodiment. [Figure 4] 3 is a flowchart of the operation of the map use system according to the embodiment. [Figure 5] FIG. 1 is a diagram schematically illustrating a map to be constructed. [Figure 6] 10 is a schematic diagram for explaining how the estimation unit estimates the self-position when a correction process is performed by the correction unit. FIG. [Figure 7] FIG. 10 is a diagram illustrating a schematic example for explaining that correction map data can be set regardless of whether or not it corresponds to an object in the real world, and that this is effective. [Figure 8] FIG. 1 is a diagram illustrating an example of a hardware configuration of a general computer. DETAILED DESCRIPTION OF THE INVENTION

[0011] 2 is a configuration diagram of a map utilization system 100 according to an embodiment. The map utilization system 100 includes a distribution server 10, an autonomous mobile robot 20, and a control device 30, which are configured to be able to communicate with each other via a network NW such as the Internet or a LAN (Local Area Network).

[0012] The autonomously moving robot 20 is a robot that moves autonomously within any indoor and / or outdoor field F, such as the premises of a particular building or a street corner area, and has the function of controlling this autonomous movement. The control device 30 controls the autonomous movement of the autonomously moving robot 20 by making partial modifications to the autonomous movement function of the autonomously moving robot 20.

[0013] The hardware that realizes the control device 30 may be a computer provided in the autonomously traveling robot 20, or may be a computer owned by a server device on a network NW that exists separately from the autonomously traveling robot 20, as schematically shown in parentheses in Figure 2.

[0014] The distribution server 10 is a server device on the network NW, which stores maps that the autonomously moving robot 20 refers to when it moves autonomously under the control of the control device 30, and distributes a map of a specific field F in which the autonomously moving robot 20 moves autonomously to the control device 30 and the autonomously moving robot 20.

[0015] 3 is a functional block diagram of a map utilization system 100 according to an embodiment. The distribution server 10 includes a map construction unit 11 and a storage unit 12. The autonomous mobile robot 20 includes a measurement unit 21, an estimation unit 22, a planning unit 23, a drive control unit 24, and a drive mechanism 25. The control device 30 includes a correction unit 31.

[0016] Fig. 4 is a flowchart of the operation of the map utilization system 100 according to the embodiment. Below, the details of the processing content of each functional block in Fig. 3 will be explained while explaining each step in Fig. 4.

[0017] In step S1, the map construction unit 11 constructs a map of the field F and stores the map in the storage unit 12 as a database, and then the process proceeds to step S2. The map construction by the map construction unit 11 can be realized by (1) automatically and / or manually generating map information using any existing method, (2) automatically and / or manually adding additional information to the map information according to this embodiment by distinguishing a correction area corresponding to a meta map, and (3) automatically and / or manually setting a correction map within the correction area.

[0018] 5 is a diagram schematically illustrating the map M constructed by the map construction unit 11. Areas within the map M are distinguished as either a normal area NA or a correction area CA according to this embodiment. Any existing method may be used to create map information within the normal area NA, which is indicated by a white background in the diagram. In this example, normal map data m1, m2, m3, and m4 are shown that demarcate an L-shaped passage PA through which the autonomous mobile robot 20 is expected to move, and that constitute the map data that the autonomous mobile robot 20 refers to when estimating its own position, and that constitute the walls of the passage PA as real-world objects.

[0019] This normal map data may be provided in a format based on any existing method as reference data for enabling self-location estimation, and may be provided, for example, as shape data consisting of a combination of straight lines and curves, or as point cloud data acquired from the surface of an object such as a wall. Point cloud data generally provides information on the position coordinates of each point as continuous values ​​such as floating points, so in order to reduce the calculation load when matching these continuous values ​​in self-location estimation, the normal map data may be provided in a format such as voxels, in which the points of the point cloud are not continuous but are discretized into lattice points.

[0020] Meanwhile, within the correction area CA shown in gray in the figure, correction map data cm1 and cm2 are also provided as reference data to enable the autonomous mobile robot 20 to estimate its own position while moving. This correction map data may or may not represent real-world objects. If the correction map data represents real-world objects, the correction map data can be created by replacing areas of vegetation, which would make it difficult to properly detect point clouds, with walls, as described as an example with reference to FIG. 1.

[0021] The format for providing the corrected map data may be any format based on an existing method, similar to that for normal map data, and may be, for example, shape data based on straight lines and / or curves, a continuous point cloud, or voxels discretized as lattice points.

[0022] In the explanation of FIG. 5 and the following explanation, two-dimensional planes are used as examples of the map and the area in which the autonomous mobile robot 20 moves, but either or both of these may be three-dimensional spaces. As is well known, map data in the form of straight lines and / or curves representing the boundaries of real-world objects in the case of a two-dimensional plane can be converted into planes and / or curved surfaces in the case of a three-dimensional space to represent the surfaces of real-world objects. (Note that, as mentioned above, the corrected map data does not have to correspond to the surfaces of real-world objects.)

[0023] Note that when the control device 30 is realized by a computer provided in the autonomous mobile robot 20, the control device 30 may not only function as the correction unit 31 but also as the estimation unit 22 and / or the planner 23 of the autonomous mobile robot 20 (i.e., the control device 30 may include the correction unit 31, and the estimation unit 22 and / or the planner 23). Also, while Fig. 3 illustrates a case where the control device 30 is realized by a computer provided in the autonomous mobile robot 20, and thus the autonomous mobile robot 20 includes the control device 30, as described above with reference to Fig. 2, the control device 30 may also be realized by a server device or the like on a network NW separate from the autonomous mobile robot 20.

[0024] In step S2, the map constructed in step S1 and stored in the memory unit 12 is distributed to the autonomous mobile robot 20 and the control device 30, and then the process proceeds to step S3. The distributed map may only cover the range of the field F in which the autonomous mobile robot 20 can move. Alternatively, the distribution server 10 may periodically receive current location information from the autonomous mobile robot 20, and distribute only a map of the range surrounding the current location information.

[0025] In step S3, autonomous mobile robot 20 starts autonomous traveling by using the map delivered in step S2 as a reference map for estimating its own position, and the process proceeds to step S4. As can be seen from the structure of the flowchart, from step S4 onwards, a decision is made at a predetermined processing timing (for example, every second), and depending on the decision, the process proceeds to step S5 or step S6, after which autonomous mobile robot 20 continues autonomous traveling in real time.

[0026] Here, an existing method can be used for the autonomous driving in step S5, and the autonomous driving in step S6 is the autonomous driving of the existing method corrected by the correction unit 31. Therefore, below, we will first explain the autonomous driving of the existing method realized by the measurement unit 21, the estimation unit 22, the planner 23, the drive control unit 24, and the drive mechanism 25.

[0027] The measurement unit 21 measures and acquires spatial data of the surrounding environment of the autonomous mobile robot 20, and outputs the spatial data to the estimation unit 22. The spatial data acquired by the measurement unit 21 may be point cloud data or voxel data obtained by discretizing this data, which can be matched with a map distributed from the distribution server 10. As a hardware configuration for acquiring point cloud data, the measurement unit 21 may be equipped with a LiDAR measurement device and acquire the point cloud. Alternatively, the measurement unit 21 may be equipped with an RGB camera or an RGB-D camera (a camera that acquires RGB images by linking depth) as hardware, and acquire the point cloud by applying SfM (3D structure reconstruction from images based on the principle of stereo vision) to the acquired images.

[0028] For the sake of explanation, it is assumed below that a point cloud is acquired as spatial data by the measurement unit 21, but as mentioned above, it is also possible to use voxels, which are discretized from the point cloud, and similar processing is possible.

[0029] The estimation unit 22 estimates the self-position using at least the SLAM method (first positioning method), which is an existing method. That is, as the first positioning method, the estimation unit 22 compares the point cloud acquired by the measurement unit 21 with the map distributed from the distribution server 10 to obtain an estimation result of the current self-position of the autonomous mobile robot 20 and information on the estimation accuracy. In this comparison, the position and orientation on the map at which the acquired point cloud best matches can be evaluated by least squares error or the like to determine the degree of matching, and the best-matched position and orientation are taken as the self-position estimation result of the autonomous mobile robot 20. The smaller the evaluated least squares error or the like, the higher the estimation accuracy, and information on the estimation accuracy can also be obtained.

[0030] As an optional embodiment, the estimation unit 22 may obtain an estimation result of its own position by using the existing SLAM method (first positioning method) in combination with one or more other second positioning methods. For example, as one of the second positioning methods, a Kalman filter or the like may be applied to the history of the self-position estimation results obtained by the first positioning method up to the present, thereby obtaining a result that reduces the influence of errors contained in the results of the first positioning method. For example, as one of the second positioning methods, the movement information (movement distance and movement direction) of the autonomous mobile robot 20 may be estimated by odometry using the rotation amount and orientation of the wheels of the autonomous mobile robot 20, and a weighted sum of the positioning results obtained by the first positioning method and the second positioning method may be calculated so that the positioning results are consistent with each other. When odometry is used, the rotation amount and orientation may be obtained from a rotation sensor (not shown) or orientation acquisition means (not shown) for the wheels constituting the drive mechanism 25, which will be described later.

[0031] The results obtained by the estimation unit 22 are output to the correction unit 31, the planning unit 23, and the drive control unit 24.

[0032] The planning unit 23 determines a route plan and driving conditions along the route (for example, setting the driving speed, including whether to make a temporary stop, and setting the minimum distance to be maintained between the autonomous robot 20 and an obstacle for the purpose of preventing a collision between the autonomous robot 20 and the obstacle, etc.) as planning information for the autonomous robot 20 to reach a destination that has been set in advance depending on the intended use (for example, delivery of goods) when the autonomous robot 20 is driving autonomously, from the self-position in the self-position estimation result obtained from the estimation unit 21, and outputs the plan to the drive control unit 24.

[0033] Drive mechanism 25 is composed of wheels, which are an example of a driving mechanism of autonomous mobile robot 20, motors, which are an example of actuators that generate the torque of the wheels and the power required to change the direction of the wheels, and gears, etc., which are an example of a mechanism that transmits the driving force, etc. from the motors to the wheels. Drive control unit 24 outputs control signals to drive mechanism 25 to control the rotation speed of the motors, etc. of drive mechanism 25 and the direction of the wheels (whether to go straight or to the left or right and to what extent).

[0034] As described above, when the estimation unit 22 uses odometry, it is sufficient to use the measurement results of sensors (not shown) installed in the drive mechanism 25.

[0035] The above has explained autonomous traveling that is realized by the components from the measurement unit 21 to the drive mechanism 25 and conforms to existing methods, so we will return to the explanation of the flowchart in FIG.

[0036] In step S3, as described above, autonomous driving is started in accordance with the existing method, and then the process proceeds to step S4. In step S4, the correction unit 31 determines whether at least one of the following is true: (1) whether the current position falls within the correction area, or (2) whether the current self-position estimation accuracy is low as determined by a predetermined threshold. If the above is true, the process proceeds to step S6, and if the above is not true, the process proceeds to step S5.

[0037] The determinations (1) and (2) in step S4 may be made as follows: For the sake of explanation, each time in step S4, which is repeated at a predetermined timing (for example, every second), is represented by an integer time t=1, 2, 3..., where the current time in this step S4 is t and the time in the previous step S4 immediately before that is t-1.

[0038] (1) Does the current location fall within the correction area? The estimation unit 21 determines whether the current position P(t) estimated at the current time t by normal self-position estimation (which may be the first positioning method alone or the second positioning method in combination) using a method that does not follow the correction instructions from the correction unit 31 is within the correction area on the map distributed from the distribution server 10.

[0039] (2) Is the current self-location estimation accuracy low according to a predetermined threshold? The estimation unit 21 determines whether the estimation accuracy, estimated by the estimation error of the current position P(t) estimated by a normal self-position estimation method (which may be the first positioning method alone or the second positioning method in combination) using a method that does not follow the correction instructions from the correction unit 31 at the current time t, is low by threshold judgment.

[0040] In addition, the determination of (2) may be made when the condition "the current self-location estimation accuracy is low based on a predetermined threshold" is true, and further when the condition (2b) "the current location falls in the vicinity of the correction area" is true. Whether or not the current location is in the vicinity may be determined by determining whether or not the distance between the current location P(t) estimated by the estimation unit 21 at the current time t using a normal self-location estimation method that does not follow the correction instruction from the correction unit 31 (this may be the first positioning method alone, or the second positioning method may be used in combination) and the correction area on the map distributed from the distribution server 10 is small based on a threshold. The significance of imposing (2b) as an additional condition is as follows. (Note that the details of the correction will be described later.)

[0041] That is, if the determination is made only based on (2), when the current position is far from the correction area and the field of view of autonomous mobile robot 20 (the field of view of measurement unit 21) does not cover the correction area at all, the self-position estimation accuracy may actually decrease accidentally for a reason completely different from the cause of the decrease in self-position estimation accuracy assumed in this embodiment as described in Fig. 1, and even if the correction described below is performed, the effect of the correction may not be obtained. In contrast, by determining based on both (2) and (2b), when the current position is near the correction area, even if the field of view of autonomous mobile robot 20 (the field of view of measurement unit 21) covers the correction area, even though it is outside the correction area and not inside the correction area, the estimation accuracy can be improved by applying the correction described below in this embodiment in a situation where the self-position estimation accuracy may decrease to some extent.

[0042] That is, by making a judgment based on (2) and (2d) instead of only (2), it is possible to actually make a correction when there is a higher possibility that applying the correction according to this embodiment (described later) will have the effect of improving estimation accuracy.

[0043] In step S5, the autonomous mobile robot 20 performs autonomous traveling in accordance with the existing method as described above, and then proceeds to step S7. Note that when making the determination in step S4, the estimation result P(t) of the self-position at the current time t has already been obtained, so in step S5, it is sufficient to perform only the processing of autonomous traveling using the estimation result P(t) as the self-position, without re-estimating the self-position at the current time t.

[0044] If step S6 is reached, it is assumed that the estimation result P(t) of the normal self-position estimation by the estimation unit 21 for the current time t, which was used when making the determination in step S4 and which is a method that does not follow the correction instruction from the correction unit 31, is low in accuracy. Therefore, by issuing a correction instruction from the correction unit 31 to the estimation unit 21, the estimation unit 21 performs self-position estimation again, which involves correction processing, to obtain a corrected self-position estimation result P(t)_corrected, and this corrected position P(t)_corrected is used as the self-position, thereby performing autonomous traveling for the current time t.

[0045] 6 is a schematic diagram for explaining how the estimation unit 21 estimates its own position when correction processing by the correction unit 31 is involved. Data D is an example of point cloud data D as raw data measured by the measurement unit 21 at the current time t, and this data D is collated with a map to estimate the own position P(t) of the object to be determined in step S4. The data D is assumed to include data D_out outside the correction area CA and data D_in inside the correction area CA as data defined on the map with the current position P(t) as the reference. (Note that in practice, either one of these cases may be present.)

[0046] 6, it is expected that the data D_in within the correction area CA will have a lot of noise or will be obtained only sparsely due to vegetation, etc. Therefore, the correction unit 31 replaces the data D_in within the correction area CA, out of all the data D from the measurement unit 21, with corrected map data cm1 and cm2 that are predefined within the correction area CA, and then instructs the estimation unit 21 to redo self-location estimation using the replaced and corrected data D_corrected.

[0047] Although the corrected map data cm1 and cm2 are illustrated as shape data using straight lines in the figure, the format may be a point cloud or voxel instead of shape data.

[0048] The estimation unit 21 considers that the data from the measurement unit 21 for the current time t is not raw data D but corrected data D_corrected, and performs self-position estimation again (the second positioning method may be used in addition to the first positioning method), thereby obtaining a corrected self-position estimation result P(t)_corrected.

[0049] In step S7, the current time t is updated to the next time t+1, and the process returns to step S4, and the above processing is repeated.

[0050] As described above, in this embodiment, by using a simple additional process of performing correction processing when the current position is expected to be within the correction area according to the flow of Fig. 4, it is possible to allow an existing autonomous mobile robot 20 that uses a point cloud matching method with a map to smoothly perform autonomous traveling. Note that although the flow of Fig. 4 does not depict an end case, if the destination is reached or if some event that requires confirmation occurs during autonomous traveling (such as the autonomous mobile robot 20 being unable to move), the process can be ended and switched to the necessary processing thereafter.

[0051] Various supplementary examples, additional examples, alternative examples, etc. will be described below.

[0052] (1) According to an embodiment of the present invention, it is possible to deal with situations where autonomous robots may have difficulty moving, thereby expanding the scope of smooth use of autonomous robots, which have been actively developed in recent years, and contributing to Goal 9 of the United Nations-led Sustainable Development Goals (SDGs), which is to “build resilient infrastructure, promote inclusive and sustainable industrialization, and promote industrial and technological innovation.”

[0053] (2) Corrected map data can be set regardless of whether it corresponds to an object in the real world, as shown in the schematic example of Fig. 7. Fig. 7 shows an example in which, in addition to corrected map data m1 and m2 representing walls corresponding to the situation in which plants are arranged alongside the walls of the passage PA in the example described above within the correction area CA, circular corrected map data m3 is set to prevent the autonomous mobile robot 20 from moving, even though only the passage PA exists in the real world and the autonomous mobile robot 20 can move freely.

[0054] For example, in the passage PA, the inside of this circular corrected map data m3 is an area where the autonomous mobile robot 20 can move freely, but it is expected that children will frequently play there, so that the autonomous mobile robot 20 can be prevented from approaching children and causing trouble.

[0055] Similarly, in a situation that corresponds to a vast plaza in the real world, where the autonomous mobile robot 20 cannot acquire a point cloud using LiDAR or the like and would otherwise lose (lose sight of) its self-position estimation result, by setting this as a correction area in this embodiment and setting correction map data as a virtual object, the autonomous mobile robot 20 can use this virtual object as a landmark and move beyond the correction area without losing its self-position estimation.

[0056] (3) As described above, the correction unit 31 performs correction when at least one of the following conditions is met: (Condition 1) the current location falls within the correction area; or (Condition 2) the current self-location estimation accuracy is low as determined by a predetermined threshold. Using both of these conditions can be expected to produce the following effects. For example, if Condition 1 is met and the current location is within the correction area, but Condition 2 is not met and the self-location estimation accuracy is high, the processing can be simplified by omitting correction because the location is not affected by noise, even within the correction area. By performing correction only when Condition 2 is met after Condition 1 is met, it becomes possible to appropriately perform correction only when it is necessary.

[0057] (4) FIG. 8 is a diagram showing an example of the hardware configuration of a general computer device 70. The control device 30 and the autonomous mobile robot 20 (excluding the drive mechanism 25, the same applies below) in the map utilization system 100 can be realized as one or more computer devices 70 having such a configuration. When the control device 30 and the autonomous mobile robot 20 are realized using two or more computer devices 70, information required for processing may be transmitted and received via a network. The computer device 70 includes a CPU (Central Processing Unit) 71 that executes predetermined instructions, a GPU (Graphics Processing Unit) 72 as a dedicated processor that executes some or all of the CPU 71's execution instructions in place of or in cooperation with the CPU 71, a RAM 73 as a main memory device that provides a work area for the CPU 71 (and GPU 72), a ROM 74 as an auxiliary memory device, a communication interface 75, a display 76 that displays information, an input interface 77 that accepts user input via a mouse, keyboard, touch panel, etc., a speaker 78 that outputs audio, and a bus BS for transmitting and receiving data among these devices.

[0058] Each functional unit in the control device 30 and the autonomous mobile robot 20 can be realized by a CPU 71 and / or a GPU 72 that reads from a ROM 74 and executes a predetermined program corresponding to the function of each unit. Both the CPU 71 and the GPU 72 are types of computing devices (processors). Here, when display-related processing is performed, a display 76 also operates in conjunction with the CPU 71 and / or the GPU 72. When communication-related processing related to data transmission and reception is performed, a communication interface 75 also operates in conjunction with the CPU 71 and the GPU 72. When audio output-related processing is performed, a speaker 78 also operates in conjunction with the CPU 71 and the GPU 72. [Explanation of symbols]

[0059] 100...map utilization system, 10...distribution server, 20...autonomous traveling robot, 30...control device 11...map construction unit, 12...storage unit, 21...measurement unit, 22...estimation unit, 23...planning unit, 24...drive control unit, 25...drive mechanism, 31...correction unit

Claims

1. A control device for an autonomous traveling robot, the autonomous mobile robot is configured to estimate its own position based at least on a first positioning result obtained by comparing spatial data of a surrounding environment measured by the autonomous mobile robot with given map information; When it is determined that the accuracy of the first positioning result is low, or when the result of the self-location estimation corresponds to a location within a predetermined area, A control device characterized by replacing areas of spatial data of the surrounding environment measured by the device itself that are pre-set as areas where a decrease in measurement accuracy is expected with given data, and then controlling the data to be compared with the given map information.

2. When it is determined that the accuracy of the first positioning result is low and the result of the self-location estimation corresponds to a location near a predetermined area, 2. The control device according to claim 1, wherein the control device replaces a portion of the spatial data of the surrounding environment measured by the device itself that is preset as a portion where a decrease in measurement accuracy is expected with given data, and then controls the data to be compared with the given map information.

3. The control device of claim 1 , wherein the given replacement data includes real-world objects corresponding to objects for which the spatial data is measured with low accuracy.

4. The control device according to claim 1 , wherein the given data to be replaced includes data that does not have a corresponding object in the real world but is set as reference data for the self-location estimation.

5. The control device according to claim 1, characterized in that the autonomous mobile robot is configured to estimate its own position based at least on the first positioning result and a second positioning result obtained by estimating its own movement.

6. A method for controlling an autonomous mobile robot executed by a computer, comprising: the autonomous mobile robot is configured to estimate its own position based at least on a first positioning result obtained by comparing spatial data of a surrounding environment measured by the autonomous mobile robot with given map information; When it is determined that the accuracy of the first positioning result is low, or when the result of the self-location estimation corresponds to a location within a predetermined area, A control method characterized by replacing areas of spatial data of the surrounding environment measured by the device itself that are pre-set as areas where a decrease in measurement accuracy is expected with given data, and then controlling the data to be compared with the given map information.

7. A program that causes a computer to function as the control device according to any one of claims 1 to 5.

Images