State estimation device and moving body control device

Abstract

To provide a technology capable of appropriately calculating the reliability of an estimation of a moving state of a moving body.SOLUTION: A state estimation device includes a state estimation unit and a reliability calculation unit. The state estimation unit estimates a moving state of a moving body based on sensor information of the moving body. The state estimation unit estimates a variance probability distribution, which is a distribution of the occurrence probability of the variance, based on the variance of the moving state. The reliability calculation unit calculates the reliability of the estimation of the moving state based on the variance probability distribution.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a state estimation device and a mobile object control device. [Background technology]

[0002] Various techniques have been proposed for devices that estimate the moving state of a moving object. For example, Patent Document 1 proposes a technique for calculating the reliability of the estimation of the position of a moving object from the ratio of a first score that indicates the degree of agreement between a central point group and a reference point group to a second score that indicates the degree of agreement between a virtual point group set around the central point group and a reference point group. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-123568 A Summary of the Invention [Problem to be solved by the invention]

[0004] The technique of Patent Document 1 has a problem in that the calculation cost is relatively high because it is necessary to calculate the degree of coincidence of the point groups for each of the first score and the second score.

[0005] Therefore, the present disclosure has been made in consideration of the above-mentioned problems, and has an object to provide a technology capable of appropriately calculating the reliability of an estimation of a moving state of a moving object. [Means for solving the problem]

[0006] The state estimation device according to the present disclosure includes a state estimation unit that estimates a moving state of a moving body based on sensor information of the moving body and estimates a variance probability distribution, which is a distribution of the occurrence probability of the variance, based on the variance of the moving state, and a reliability calculation unit that calculates a reliability of the estimation of the moving state based on the variance probability distribution. Effect of the Invention

[0007] According to the present disclosure, a variance probability distribution is estimated based on the variance of the moving state, and a reliability of the estimation of the moving state is calculated based on the variance probability distribution. With this configuration, it is possible to appropriately calculate the reliability of the estimation of the moving state of the moving body. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a configuration of a state estimating device according to a first embodiment. [Diagram 2] 4 is a diagram for explaining the operation of a state estimation unit according to the first embodiment. FIG. [Diagram 3] 4 is a diagram for explaining the operation of a state estimation unit according to the first embodiment. FIG. [Figure 4] FIG. 4 is a diagram showing an example of a distributed probability distribution according to the first embodiment. [Diagram 5] FIG. 11 is a diagram for explaining the operation of a reliability calculation unit according to the second modification of the first embodiment. [Figure 6] FIG. 13 is a diagram for explaining the operation of a reliability calculation unit according to the third modification of the first embodiment. [Figure 7] FIG. 13 is a diagram for explaining the operation of a reliability calculation unit according to the fourth modification of the first embodiment. [Figure 8] FIG. 11 is a block diagram showing a configuration of a moving object control device according to a second embodiment. [Figure 9] 13 is a diagram for explaining the operation of a reliability recovery unit according to the second embodiment. FIG. [Figure 10] 13(a) to 13(c) are diagrams illustrating the operation of a reliability recovery unit according to a first modification of the second embodiment. [Figure 11] FIG. 13 is a block diagram showing a hardware configuration of a state estimating device according to another modified example. [Figure 12] FIG. 13 is a block diagram showing a hardware configuration of a state estimating device according to another modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] <Embodiment 1> Fig. 1 is a block diagram showing a configuration of a state estimation device 1 according to the present embodiment 1. The state estimation device 1 in Fig. 1 includes a state estimation unit 11, a statistical information update unit 12, a storage unit 13, a reliability calculation unit 14, and a first determination unit 15 which is a determination unit, and is connected to a sensor 51.

[0010] The sensor 51 detects a point cloud indicating obstacles around the moving body, and generates information of the point cloud as sensor information of the moving body. The moving body is, for example, a movable object such as a vehicle or a robot. The sensor 51 is, for example, a millimeter wave radar, a LiDAR (Light Detection And Ranging), or the like.

[0011] The state estimation unit 11 estimates the moving state of the moving body based on the sensor information of the moving body, and estimates a variance probability distribution, which is a distribution of the occurrence probability of variance, based on the variance of the moving state. The variance probability distribution indicates the occurrence tendency of variance. In the following, a case where the moving state is a combination of the position (x, y) of the moving body in a planar view and the orientation (θ) of the moving body will be described, but the present invention is not limited to this.

[0012] The state estimation unit 11 will be described in detail. Fig. 2 is a diagram showing an example in which the state estimation unit 11 estimates the moving state of a moving object based on sensor information using an AMCL (Adaptive Monte Carlo Localization) particle filter. First, the state estimation unit 11 randomly arranges a plurality of particles (corresponding to arrows in Fig. 2) within a certain range in a virtual space, and acquires an obstacle position 21 as seen from each particle based on the sensor information. Then, the state estimation unit 11 calculates the distance between the acquired obstacle position 21 and an obstacle position 22 in map information prepared in advance in the state estimation unit 11 or the like, as a likelihood.

[0013] The state estimation unit 11 generates a new arrangement of particles by leaving particles having a likelihood equal to or greater than the threshold as they are and rearranging particles having a likelihood less than the threshold around the remaining particles or randomly arranging them. The state estimation unit 11 performs the above operations recursively until the arrangement of the new particles converges to a certain extent.

[0014] The state estimation unit 11 estimates a state probability distribution, which is a probability distribution of the moving state of the moving body, based on the distribution of the multiple particles that has converged to a certain extent. For example, the state estimation unit 11 estimates the state probability distribution by taking the number of particles per unit area as the probability of the moving state of the moving body. Note that the state estimation unit 11 may estimate the state probability distribution by taking into account not only the distribution of the multiple particles but also other sensor information and a robot model.

[0015] The state estimation unit 11 estimates state probability distributions at multiple points in time by performing estimation based on the above-mentioned sensor information for multiple points in time. In Fig. 3, a graph of the state probability distribution at one point in time is shown, and state probability distributions 23 at three points in time are diagrammatically shown by ellipses.

[0016] The state estimation unit 11 calculates variances at multiple time points from the state probability distribution 23 at multiple time points. When the moving state is expressed as x, y, and θ, the state estimation unit 11 calculates a covariance matrix shown in the following formula (1) from the state probability distribution 23 for each time point, and calculates the diagonal sum of the covariance matrix (=S xx +S yy +S θθ ) is calculated as the variance.

[0017]

number

[0018] The state estimation unit 11 estimates a variance probability distribution, which is a distribution of the occurrence probability of variances, based on the variances at multiple points in time. For example, the state estimation unit 11 calculates the variance probability distribution by calculating the number of variances of a certain value / the total number of variances as the occurrence probability of a variance of a certain value.

[0019] 4 is a diagram showing an example of a variance probability distribution according to the first embodiment. The horizontal axis of the variance probability distribution is the variance, and the vertical axis of the variance probability distribution is the probability of the occurrence of the variance. When the number of multiple time points, that is, the number of samples of the variance, is sufficiently large, the variance probability distribution as shown in FIG. 4 is estimated.

[0020] The variance probability distribution may be modeled by a distribution function. The distribution function used for modeling may be, for example, any of the following distribution functions: normal distribution, log-normal distribution, and chi-square distribution. FIG. 4 shows a curve indicated by the distribution function of the log-normal distribution, and the variance probability distribution is appropriately represented by the curve. When the variance probability distribution is modeled by a distribution function, the amount of data required to represent the variance probability distribution can be reduced, and therefore the calculation cost of the reliability described below can be reduced.

[0021] On the other hand, the variance probability distribution may be expressed as a table or the like without being modeled by a distribution function, for example, the histogram data of Fig. 4. With such a configuration, the amount of data and the calculation cost will be somewhat larger than when modeled by a distribution function, but since there is no error caused by modeling, the accuracy of the reliability described below can be improved.

[0022] In the first embodiment, before the mobile object is introduced into the facility, that is, during the preliminary travel of the mobile object, the state estimation unit 11 in Fig. 1 estimates the moving state of the mobile object based on the sensor information as described above, and estimates the variance probability distribution based on the variance of the moving state as described above. The statistical information update unit 12 stores the variance probability distribution thus estimated by the state estimation unit 11 based on the sensor information during the preliminary travel of the mobile object in the storage unit 13 as a stationary variance probability distribution.

[0023] After the moving object is introduced into the facility, the state estimation unit 11 estimates the moving state of the moving object based on the sensor information at any time and calculates the variance of the moving state at any time. The state estimation unit 11 according to the first embodiment does not estimate the variance probability distribution based on the variance of the moving state after the moving object is introduced into the facility, but may estimate the variance probability distribution as in Modifications 1 and 2 described later.

[0024] The reliability calculation unit 14 calculates the reliability of the estimation of the moving state based on the variance estimated by the state estimation unit 11 and the stationary variance probability distribution stored in the storage unit 13. When the number of samples of the variance is sufficiently large, the reliability calculation unit 14 may calculate the reliability using either the following first or second example.

[0025] As a first example, the reliability calculation unit 14 judges whether the variance value estimated by the state estimation unit 11 is equal to or greater than the average value of the variance of the stationary variance probability distribution. If it is determined that the variance value estimated by the state estimation unit 11 is smaller than the average value, the reliability calculation unit 14 acquires a predetermined default value as the reliability. If it is determined that the variance value estimated by the state estimation unit 11 is equal to or greater than the average value, the reliability calculation unit 14 acquires, as the reliability, the probability corresponding to the variance value estimated by the state estimation unit 11, from the stationary variance probability distribution.

[0026] As a second example, the reliability calculation unit 14 calculates, as the reliability, a value obtained by "1-sum of the probabilities of all variance values ​​smaller than the variance value estimated by the state estimation unit 11." In other words, the reliability calculation unit 14 calculates, as the reliability, the area of ​​the part of the graph of the stationary variance probability distribution that is equal to or larger than the variance value estimated by the state estimation unit 11.

[0027] In the above description, the reliability calculation unit 14 calculates the reliability based on the variance estimated by the state estimation unit 11 and the stationary variance probability distribution stored in the memory unit 13. However, as will be described in Modification Example 2 below, the reliability calculation unit 14 may calculate the reliability based on the variance probability distribution (specifically, the sequential variance probability distribution and the stationary variance probability distribution) without using the variance estimated by the state estimation unit 11.

[0028] The first determination unit 15 determines whether the moving state is correct or not based on the reliability calculated by the reliability calculation unit 14. For example, when the reliability calculated by the reliability calculation unit 14 is equal to or greater than a threshold, the first determination unit 15 determines that the moving state is correct, and when the reliability calculated by the reliability calculation unit 14 is less than the threshold, the first determination unit 15 determines that the estimation of the moving state has failed.

[0029] The state estimation device 1 may perform various processes based on the determination result of the first determination unit 15. For example, the state estimation device 1 may perform the above operations for each of the multiple sensors 51, and estimate the moving state using the sensor 51 whose moving state is determined to be correct, without using the sensor 51 whose moving state is determined to be incorrect. In addition, for example, the state estimation device 1 may notify the determination result of the first determination unit 15 to the outside using at least one of sound, light, and wireless communication. In this specification, for example, at least one of A, B, C, ..., and Z means any one of all combinations of one or more types extracted from the group of A, B, C, ..., and Z.

[0030] <Summary of the first embodiment> According to the state estimation device 1 of the first embodiment as described above, a variance probability distribution is estimated based on the variance of the moving state, and the reliability of the estimation of the moving state is calculated based on the variance probability distribution. With such a configuration, the reliability is calculated based on the variance probability distribution, which has low calculation cost and is likely to reflect the parameter settings and the traveling environment, so that the reliability can be calculated appropriately.

[0031] <Variation 1> The state estimation unit 11 in embodiment 1 estimates the moving state and the variance probability distribution before the moving body is introduced into the facility, and the statistical information update unit 12 stores the variance probability distribution in the memory unit 13 as a stationary variance probability distribution, but this is not limited to this.

[0032] For example, after a moving object is introduced into a facility, the state estimation unit 11 may estimate both the moving state and the variance probability distribution while using the latest map information, and the statistical information update unit 12 may update the stationary variance probability distribution in the storage unit 13 based on the variance probability distribution. With this configuration, even if the layout of the facility is changed after the moving object is introduced into the facility, it is possible to calculate a reliability suitable for the new layout after the stationary variance probability distribution is updated.

[0033] <Variation 2> The state estimation unit 11 may sequentially perform the process of Modification 1 after the moving object is introduced into the facility. In other words, after the moving object is introduced into the facility, the state estimation unit 11 may sequentially estimate the moving state and the variance probability distribution while using the latest map information based on sensor information from a predetermined past period from the current time. Hereinafter, the variance probability distribution estimated sequentially in this way is referred to as a sequential variance probability distribution.

[0034] The reliability calculation unit 14 may calculate the overlap rate between the sequential distributed probability distribution and the stationary distributed probability distribution stored in the storage unit 13 during the preliminary travel of the moving object as the reliability. For example, the reliability calculation unit 14 may obtain the area of ​​the overlapping portion 26 between the sequential distributed probability distribution and the stationary distributed probability distribution in FIG. 5. Then, the reliability calculation unit 14 may calculate the value obtained by dividing the overlapping portion 26 by the total area (for example, the first area of ​​the sequential distributed probability distribution 27, the second area of ​​the stationary distributed probability distribution 28, or the average area of ​​the first area and the second area) as the reliability. With this configuration, the influence of fluctuations in the moving state immediately before the calculation can be suppressed, so that the reliability can be calculated with high accuracy.

[0035] In addition, since variances at multiple points in time are required to obtain the sequential variance probability distribution 27, the above configuration takes some time to calculate the reliability. Therefore, the reliability calculation unit 14 may move the stationary variance probability distribution 28 so that the center position (e.g., average value) of the stationary variance probability distribution 28 becomes the current variance value, and calculate the overlap rate between the stationary variance probability distribution 28 after the movement and the stationary variance probability distribution 28 before the movement as the reliability. With such a configuration, the reliability is calculated without using the sequential variance probability distribution 27, so that the responsiveness of the reliability calculation can be improved.

[0036] <Modification 3> The state estimation unit 11 according to the first embodiment calculates the diagonal sum (=S xx +S yy +S θθ ) is calculated as the variance, but this is not limited to this. For example, the state estimation unit 11 may calculate the variance (=S xx +S yy ) and the variance in the rotational direction of the moving object (=S θθ 6 , a variance probability distribution 31 in the translation direction and a variance probability distribution 32 in the rotation direction may be estimated. Then, the reliability calculation unit 14 may calculate the reliability of the translation direction and the rotation direction based on the variance probability distributions 31 and 32 in the translation direction and the rotation direction, respectively.

[0037] According to such a configuration, the first determination unit 15 can determine whether the moving state is correct for each of the translation direction and the rotation direction. Also, the reliability recovery unit described in the second embodiment can handle each of the translation direction and the rotation direction individually (for example, limit the speed).

[0038] <Variation 4> The state estimation unit 11 may estimate a variance probability distribution for a plurality of predetermined locations and store it in the storage unit 13. The plurality of predetermined locations may include, for example, a narrow location, a location with few features, a location with many obstacles, and the like.

[0039] Then, when the moving object is located at one predetermined location, the reliability calculation unit 14 may calculate the reliability based on the variance probability distribution estimated for the one predetermined location.

[0040] 7, when the state estimation device 1 determines that the moving object is located in a narrow place based on the sensor information, the reliability calculation unit 14 may calculate the reliability based on a variance probability distribution 36 estimated for a wide place. On the other hand, when the state estimation device 1 determines that the moving object is located in a narrow place based on the sensor information, the reliability calculation unit 14 may calculate the reliability based on a variance probability distribution 37 estimated for a wide place.

[0041] According to this configuration, it is possible to calculate a reliability suitable for a predetermined location. In Fig. 7, the variance probability distribution 37 in the narrow location has a shorter length in the short side direction of the ellipse than the variance probability distribution 36 in the wide location. However, since the variance probability distributions 36, 37 are distributions of the probability of occurrence of variance rather than state probability distributions, the variance probability distribution 37 in the narrow location may have a longer length in the short side direction of the ellipse than the variance probability distribution 36 in the wide location.

[0042] <Embodiment 2> 8 is a block diagram showing the configuration of a mobile object control device 41 according to the present embodiment 2. In the following, among the components according to the present embodiment 2, components that are the same as or similar to the components described above are given the same or similar reference numerals, and different components will be mainly described.

[0043] The mobile object control device 41 in Fig. 8 includes the state estimation unit 11, the statistical information update unit 12, the storage unit 13, and the reliability calculation unit 14 in Fig. 1, that is, some of the components of the state estimation device 1 in Fig. 1. The mobile object control device 41 also includes a second determination unit 42 and a reliability recovery unit 43.

[0044] The second determination unit 42 determines whether the time during which the reliability calculated by the reliability calculation unit 14 is less than the threshold continues for a predetermined time or longer.

[0045] The reliability recovery unit 43 recovers the reliability based on the reliability when it is determined that the time during which the reliability is less than the threshold has continued for a predetermined time or more. On the other hand, when it is not determined that the time during which the reliability is less than the threshold has continued for a predetermined time or more, the reliability recovery unit 43 does not perform the process of recovering the reliability based on the reliability.

[0046] The reliability recovery unit 43 according to the second embodiment recovers reliability by controlling the speed of the moving object based on the reliability. For example, as shown in FIG. 9, the reliability recovery unit 43 may control the speed of the moving object to be "α×reliability" for a certain range of reliability, with α being a constant greater than 0. With this configuration, the lower the reliability, the lower the speed of the moving object can be reduced. Note that in the example of FIG. 9, the lower limit of the speed of the moving object is greater than 0, but it may be 0.

[0047] <Summary of the second embodiment> The mobile body control device 41 according to the second embodiment as described above includes a portion of the configuration of the state estimation device 1 according to the first embodiment, and therefore can appropriately calculate the reliability of the estimation of the moving state of the mobile body, similar to the first embodiment.

[0048] According to the second embodiment, the reliability recovery unit 43 recovers the reliability by controlling the speed of the moving object based on the reliability. With this configuration, it is possible to appropriately control the movement of the moving object.

[0049] <Variation 1> In the second embodiment, the reliability recovery unit 43 recovers the reliability by controlling the speed of the moving object based on the reliability, but the invention is not limited to this. For example, the reliability recovery unit 43 may recover the reliability by making the state estimation unit 11 perform a fitting process based on a particle filter and the variance of the moving state based on the reliability.

[0050] 10(a) to 10(c) are diagrams for explaining an example of fitting processing based on the AMCL particle filter and a covariance matrix (i.e., variance of the moving state) for an obstacle position 21 based on sensor information and an obstacle position 22 on map information. Note that the variance of the moving state, i.e., the covariance matrix of the moving state, corresponds to the shape of the state probability distribution 23 shown in FIG. 10(a) to 10(c).

[0051] 10(a), the position 21 of the obstacle based on the sensor information is misaligned with the position 22 of the obstacle on the map information. In this case, the shape of the state probability distribution 23 extends in the direction of the passage defined by the wall, which is an obstacle, more than the shape of the normal state probability distribution 23, so the reliability calculated by the reliability calculation unit 14 decreases.

[0052] When it is determined that the time during which the reliability is less than the threshold continues for a predetermined time or more, the reliability recovery unit 43 causes the state estimation unit 11 to perform a fitting process based on the particle filter and variance. In the fitting process, the state estimation unit 11 uses an arrangement of particles randomly arranged in correspondence with the shape of the state probability distribution 23 (i.e., the covariance matrix of the moving state). In FIG. 10(b), since the shape of the state probability distribution 23 extends in the moving direction of the moving body, the particles are arranged in a distribution extending in the moving direction of the moving body. The state estimation unit 11 obtains the particle arrangement of FIG. 10(c) by repeating the weighting of the particle filter and resampling in the same manner as described above using the particle arrangement of FIG. 10(b).

[0053] 10(c) becomes the shape of a normal state probability distribution 23, the reliability calculated by the reliability calculation unit 14 increases, and the reliability is restored. With this configuration, it is possible to prevent a decrease in reliability without performing a prediction step of the particle filter and without controlling the movement of the moving object based on the reliability.

[0054] <Variation 2> In the second embodiment, the mobile object control device 41 includes the second determination unit 42, but the present invention is not limited to this. For example, the mobile object control device 41 may be configured not to include the second determination unit 42, and the reliability recovery unit 43 may be directly connected to the reliability calculation unit 14. In this configuration, the reliability recovery unit 43 recovers the reliability based on the reliability, regardless of whether the time during which the reliability is less than the threshold continues for a predetermined time or longer.

[0055] In addition, in a configuration in which the mobile body control device 41 is equipped with a second judgment unit 42, the second judgment unit 42 may have a function similar to that of the first judgment unit 15 in Figure 1, and the state estimation device 1 may perform various processing based on the judgment result obtained by the function of the first judgment unit 15.

[0056] <Other Modifications> The state estimation unit 11 and the reliability calculation unit 14 described above are hereinafter referred to as the "state estimation unit 11, etc." The state estimation unit 11, etc. are realized by a processing circuit 81 shown in FIG. 11. That is, the processing circuit 81 includes the state estimation unit 11 that estimates the moving state of the moving body based on the sensor information of the moving body and estimates a variance probability distribution, which is a distribution of the occurrence probability of the variance, based on the variance of the moving state, and the reliability calculation unit 14 that calculates the reliability of the estimation of the moving state based on the variance probability distribution. The processing circuit 81 may be implemented with dedicated hardware or a processor that executes a program stored in a memory. The processor may be, for example, a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a DSP (Digital Signal Processor), or the like.

[0057] When the processing circuit 81 is a dedicated hardware, the processing circuit 81 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these. Each function of the state estimation unit 11 and the like may be realized by a circuit in which the processing circuits are distributed, or the functions of each unit may be realized by a single processing circuit.

[0058] When the processing circuit 81 is a processor, the functions of the state estimation unit 11 and the like are realized by a combination with software and the like. The software and the like corresponds to, for example, software, firmware, or software and firmware. The software and the like are described as a program and stored in a memory. As shown in FIG. 12, the processor 82 applied to the processing circuit 81 realizes the functions of each unit by reading and executing a program stored in a memory 83. That is, the state estimation device 1 includes a memory 83 for storing a program that, when executed by the processing circuit 81, results in the execution of a step of estimating a moving state of a moving body based on sensor information of the moving body, estimating a variance probability distribution, which is a distribution of the occurrence probability of variance, based on the variance of the moving state, and a step of calculating the reliability of the estimation of the moving state based on the variance probability distribution. In other words, this program can be said to cause a computer to execute the procedure or method of the state estimation unit 11 and the like. Here, the memory 83 may be, for example, a non-volatile or volatile semiconductor memory such as a Random Access Memory (RAM), a Read Only Memory (ROM), a flash memory, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), an HDD (Hard Disk Drive), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc), a drive device for any of these, or any storage medium to be used in the future.

[0059] The above describes a configuration in which each function of the state estimation unit 11, etc. is realized either in hardware or software, etc. However, the present invention is not limited to this, and a configuration in which a part of the state estimation unit 11, etc. is realized in dedicated hardware and another part is realized in software, etc. For example, the function of the state estimation unit 11 can be realized by a processing circuit 81 as dedicated hardware, and the function of the reliability calculation unit 14 can be realized by the processing circuit 81 as a processor 82 reading and executing a program stored in a memory 83.

[0060] As described above, the processing circuitry 81 can realize each of the above-mentioned functions by hardware, software, or a combination of these.

[0061] It should be noted that the embodiments and modifications may be freely combined, and the embodiments and modifications may be modified or omitted as appropriate.

[0062] Various aspects of the present disclosure are summarized below as appendices.

[0063] (Appendix 1) a state estimation unit that estimates a moving state of the moving object based on sensor information of the moving object, and estimates a variance probability distribution, which is a distribution of the occurrence probability of the variance, based on the variance of the moving state; a reliability calculation unit that calculates a reliability of the estimation of the moving state based on the variance probability distribution; A state estimation device comprising:

[0064] (Appendix 2) 2. A state estimation device according to claim 1, The state estimator, wherein the variance probability distribution is modeled by a distribution function.

[0065] (Appendix 3) A state estimation device according to claim 1 or 2, a storage unit configured to store the variance probability distribution estimated by the state estimation unit based on the sensor information during a preliminary travel of the moving object, The reliability calculation unit is a state estimation device that calculates, as the reliability, an overlap rate between the variance probability distribution estimated by the state estimation unit based on the sensor information for a predetermined past period from the current time and the variance probability distribution stored in the memory unit.

[0066] (Appendix 4) A state estimation device according to any one of Supplementary Note 1 to Supplementary Note 3, The state estimation unit is Estimating the variance probability distribution in each of the translation direction and the rotation direction of the moving object; The reliability calculation unit is a state estimation device that calculates the reliability of the translation direction and the rotation direction based on the variance probability distribution of the translation direction and the rotation direction, respectively.

[0067] (Appendix 5) A state estimation device according to any one of Supplementary Note 1 to Supplementary Note 4, The state estimation unit is estimating the variance probability distribution for a plurality of predetermined locations; The reliability calculation unit is A state estimation device that, when the moving body is located at one of the predetermined locations, calculates the reliability based on the variance probability distribution estimated for the one predetermined location.

[0068] (Appendix 6) A state estimation device according to any one of Supplementary Note 1 to Supplementary Note 5, The state estimation device further includes a determination unit that determines whether the moving state is correct or not based on the reliability.

[0069] (Appendix 7) A state estimation device according to any one of Supplementary Note 1 to Supplementary Note 6; a reliability recovery unit that recovers the reliability based on the reliability; A mobile control device comprising:

[0070] (Appendix 8) A moving object control device according to claim 7, The reliability recovery unit includes: A mobile object control device that recovers the reliability by controlling a speed of the mobile object based on the reliability.

[0071] (Appendix 9) A mobile object control device according to claim 7 or 8, The state estimation unit estimates the movement state based on the sensor information by using a particle filter; The reliability recovery unit includes: The mobile body control device recovers the reliability by causing the state estimation unit to perform a fitting process based on the particle filter and the variance based on the reliability. [Explanation of symbols]

[0072] 1 state estimation device, 11 state estimation unit, 13 storage unit, 14 reliability calculation unit, 15 first determination unit, 41 mobile object control device, 43 reliability recovery unit.

Claims

1. a state estimation unit that estimates a moving state of the moving object based on sensor information of the moving object, and estimates a variance probability distribution, which is a distribution of occurrence probability of the variance, based on the variance of the moving state; a reliability calculation unit that calculates a reliability of the estimation of the moving state based on the variance probability distribution; A state estimation device comprising:

2. The state estimation device according to claim 1 , The state estimator, wherein the variance probability distribution is modeled by a distribution function.

3. 3. A state estimation device according to claim 1, further comprising: a storage unit configured to store the variance probability distribution estimated by the state estimation unit based on the sensor information during a preliminary travel of the moving object, The reliability calculation unit is a state estimation device that calculates, as the reliability, an overlap rate between the variance probability distribution estimated by the state estimation unit based on the sensor information for a predetermined past period from a current time and the variance probability distribution stored in the memory unit.

4. 3. A state estimation device according to claim 1, further comprising: The state estimation unit is Estimating the variance probability distribution in each of the translation direction and the rotation direction of the moving object; The reliability calculation unit is a state estimation device that calculates the reliability of the translation direction and the rotation direction based on the variance probability distribution of the translation direction and the rotation direction, respectively.

5. 3. A state estimation device according to claim 1, further comprising: The state estimation unit is estimating the variance probability distribution for a plurality of predetermined locations; The reliability calculation unit is A state estimation device that, when the moving body is located at one of the predetermined locations, calculates the reliability based on the variance probability distribution estimated for the one predetermined location.

6. 3. A state estimation device according to claim 1, further comprising: The state estimation device further includes a determination unit that determines whether the moving state is correct or not based on the reliability.

7. A state estimation device according to claim 1 or 2; a reliability recovery unit that recovers the reliability based on the reliability; A mobile control device comprising:

8. The mobile object control device according to claim 7, The reliability recovery unit includes: A mobile object control device that recovers the reliability by controlling a speed of the mobile object based on the reliability.

9. The mobile object control device according to claim 7, The state estimation unit estimates the movement state based on the sensor information by using a particle filter; The reliability recovery unit includes: The mobile body control device recovers the reliability by having the state estimation unit perform a fitting process based on the particle filter and the variance based on the reliability.

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