Information processing device, information processing method, program, and storage medium

Abstract

To provide an information processing apparatus that can suitably reduce the data amount of measurement data to be uploaded.SOLUTION: A controller 13 of an information processing apparatus 1 acquires a measurement result for every measurement direction from a lidar 3 that is a measurement device carrying out a measurement from a fixed position. The controller 13 determines for every measurement point whether an assumed stationary object is measured on the basis of the acquired measurement result. The controller 13 determines the transmission frequency to transmit, to a data collection device 200, the measurement result at the measurement point for which the stationary object is determined to be measured, on the basis of stability at the measurement point.SELECTED DRAWING: Figure 11

Description

[Technical Field]

[0001] The present disclosure relates to processing of measured data. [Background technology]

[0002] Laser radar devices that irradiate a detection space with pulses of laser light and detect objects within the detection space based on the level of the reflected light have been known for some time. For example, Patent Document 1 discloses a lidar that scans the surrounding space by appropriately controlling the emission direction (scanning direction) of repeatedly emitted light pulses and observes the returned light to generate point cloud data representing information about surrounding objects, such as distance and reflectivity. Furthermore, Patent Document 2 discloses a technology that compares observation data obtained by a distance measuring device with distance data of background candidates stored in a background candidate data storage unit and counts up the number of observations of background candidates whose distance data matches the observation data, in order to determine an appropriate number of backgrounds based on the observation data even when the observation data from the distance measuring device is unstable. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-009831 [Patent Document 2] Japanese Patent Application Publication No. 2017-207365 Summary of the Invention [Problem to be solved by the invention]

[0004] When point cloud data generated by a measurement device such as a lidar according to a predetermined scanning cycle is uploaded and collected and managed by a server device, the volume of the generated point cloud data is large, which can cause problems such as excessive communication loads and processing loads on the server device. In particular, when the measurement device is fixed, there is a problem that the amount of data to be uploaded increases unnecessarily if data of stationary objects that are stably measured is uploaded as is.

[0005] The present disclosure has been made to solve the above-mentioned problems, and a main object of the present disclosure is to provide an information processing device that can suitably reduce the amount of measurement data to be uploaded. [Means for solving the problem]

[0006] The claimed invention is an acquisition means for acquiring a measurement result for each measurement point corresponding to a measurement direction by a measurement device that measures from a fixed position; a determination means for determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; a transmission frequency control means for determining a transmission frequency for transmitting the measurement results for the measurement point at which it is determined that the stationary object has been measured to a data collection device based on the stability of the measurement of the stationary object at the measurement point; The information processing device has the following.

[0007] The claimed invention also includes: Obtaining measurement results for each measurement point corresponding to the measurement direction by a measurement device that measures from a fixed position; determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; determining a frequency of transmitting the measurement results for the measurement point at which it is determined that the stationary object has been measured to a data collection device based on the stability of the measurement of the stationary object at the measurement point; It is an information processing method.

[0008] The claimed invention also includes: Obtaining measurement results for each measurement point corresponding to the measurement direction by a measurement device that measures from a fixed position; determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; This is a program that causes a computer to execute a process of determining the frequency with which measurement results for the measurement point at which it is determined that the stationary object has been measured should be transmitted to a data collection device, based on the stability with which the stationary object is being measured at that measurement point. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic configuration of a rider unit according to a first embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of an information processing device. [Figure 3] FIG. 1 is a diagram illustrating a space in which a lidar performs measurements. [Figure 4] 10 is a table showing an example of interpretation of the first stability. [Figure 5] 1 is a graph showing the relationship between distance measured by a lidar and time during a period of time that is the subject of generating a distance histogram at a certain measurement point. [Figure 6] FIG. 6 is a diagram showing a distance histogram and measurement time information based on FIG. 5. [Figure 7] 10 is a graph showing a measurement distance history at a certain measurement point. [Figure 8] 10 is an example of a flowchart illustrating a procedure for generating and updating stability information in the first embodiment. [Figure 9] 10 shows a schematic configuration of a data collection system according to a second embodiment. [Figure 10] 1 shows a graph representing the change over time in the measured distance at a certain measurement point and a histogram of the corresponding measured distances. [Figure 11] 10 is a flowchart illustrating an example of a procedure for upload processing according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] According to a preferred embodiment of the present invention, an information processing device includes: an acquisition unit that acquires measurement results for each measurement point corresponding to a measurement direction by a measurement device that measures from a fixed position; a determination unit that determines, for each measurement point, whether an expected stationary object has been measured based on the measurement results; and a transmission frequency control unit that determines a transmission frequency for transmitting the measurement results for the measurement point where it has been determined that the stationary object has been measured to a data collection device based on the stability of the measurement of the stationary object at the measurement point. According to this aspect, the information processing device can accurately determine the transmission frequency of the measurement results for the expected stationary object and preferably reduce the amount of data to be uploaded.

[0011] In one aspect of the information processing device, the information processing device further includes a transmitting unit that transmits, to the data collecting device, measurement results for the measurement points where it is determined that the stationary object has been measured, based on the transmission frequency, and immediately transmits, to the data collecting device, measurement results for the measurement points where it is determined that the stationary object has not been measured. With this aspect, the information processing device can suitably upload the measurement results of the measurement devices to the data collecting device.

[0012] In another aspect of the information processing device, the measuring device measures the distance for each measurement point, and the determining means determines for each measurement point whether the stationary object has been measured based on the difference between the measured distance measured by the measuring device and the assumed distance to the stationary object. With this aspect, the information processing device can preferably determine for each measurement point whether the assumed stationary object has been measured.

[0013] In another aspect of the information processing device, the determination means determines, for each measurement point, whether the stationary object has been measured based on the difference and a threshold value set based on an index of variation in the past measurement distances. With this aspect, the information processing device can accurately determine, for each measurement point, whether an expected stationary object has been measured, taking into account statistical errors, etc.

[0014] In another aspect of the information processing device, the determination means determines whether the stationary object has been measured for each measurement point based on the difference and a threshold value set based on the stability. With this aspect, the information processing device can accurately determine whether the stationary object has been measured, taking into account the stability at which the expected stationary object will be measured.

[0015] In another aspect of the information processing device, the transmission frequency control means determines the transmission frequency based on the stability determined by referring to stability information indicating at least the stability for each measurement point. This aspect allows the information processing device to accurately grasp the stability for each measurement point and appropriately determine the transmission frequency of the measurement results for each measurement point by the measurement device. In a preferred example of the information processing device, the information processing device may further include generation means for generating the stability information based on the time-series measurement results for each measurement point by the measurement device.

[0016] In another aspect of the information processing device, the stability is a first stability representing the degree to which the stationary object exists stably as a stationary object, or a second stability representing the degree to which the stationary object can be measured stably without being obstructed by other objects, or an index that combines the first stability and the second stability.

[0017] According to another preferred embodiment of the present invention, there is provided a control method executed by a computer, which acquires measurement results for each measurement point corresponding to a measurement direction of a measurement device that measures from a fixed position, determines for each measurement point whether an expected stationary object has been measured based on the measurement results, and determines the frequency of transmitting the measurement results for the measurement point where it has been determined that the stationary object has been measured to a data collection device based on the stability of measuring the stationary object at that measurement point. By executing this control method, the information processing device can suitably reduce the amount of data to be uploaded.

[0018] According to another preferred embodiment of the present invention, there is provided a program that causes a computer to execute a process of acquiring measurement results for each measurement point corresponding to a measurement direction by a measurement device that measures from a fixed position, determining for each measurement point whether an expected stationary object has been measured based on the measurement results, and determining a transmission frequency for transmitting the measurement results for the measurement point where it has been determined that the stationary object has been measured to a data collection device based on the stability of measuring the stationary object at that measurement point. By executing this program, the computer can preferably reduce the amount of data to be uploaded. Preferably, the program is stored in a storage medium. [Example]

[0019] Preferred embodiments of the present invention will now be described with reference to the drawings.

[0020] <First Example> (1) Overview of the Lidar Unit 1 is a schematic diagram of a lidar unit 100 according to a first embodiment. The lidar unit 100 includes an information processing device 1 that processes data generated by a sensor group 2, and the sensor group 2 that includes at least a lidar (Light Detection and Ranging, or Laser Illuminated Detection and Ranging) 3. The lidar unit 100 is fixedly installed indoors or outdoors, and generates information (also referred to as "stability information") that indicates stability, which is the degree to which stationary objects present within the measurement range of the lidar 3 can be stably measured.

[0021] The information processing device 1 is electrically connected to the sensor group 2 and processes data output by various sensors included in the sensor group 2. In this embodiment, the information processing device 1 generates stability information indicating at least the stability for each measurement direction within the measurement range of the LIDAR 3 based on point cloud data generated in a time series in the past and present by the LIDAR 3. The stability information is, for example, information that indicates the distance and stability of a stationary object expected to be measured for each measurement direction. The process of generating the stability information and the interpretation of the stability will be described later. The information processing device 1 is, for example, fixedly installed while being housed in a housing together with the LIDAR 3. The information processing device 1 may be provided integrally with the LIDAR 3 as an electronic control device for the LIDAR 3.

[0022] The LIDAR 3 measures the distance to an object in the external world in a discrete manner by emitting a pulsed laser beam while changing the angle within a predetermined angular range in the horizontal and vertical directions. In this case, the LIDAR 3 includes an irradiation unit that irradiates a laser beam while changing the irradiation direction (i.e., scanning direction), a light receiving unit that receives reflected light (scattered light) of the irradiated laser beam, and an output unit that outputs data based on a light receiving signal output by the light receiving unit. The data measured by the LIDAR 3 for each irradiation direction of the pulsed laser beam is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the response delay time of the laser beam identified based on the above-mentioned light receiving signal. Then, in one scanning cycle, the LIDAR 3 generates data corresponding to each measurement point in the measurement range of the LIDAR 3 (i.e., the irradiation range of the pulsed laser) as point cloud data. The LIDAR 3 is an example of a "measurement device" according to the present invention. Note that the LIDAR 3 is not limited to the above-described scan-type LIDAR, but may also be a flash-type LIDAR that generates three-dimensional data by irradiating a two-dimensional array sensor's field of view with diffused laser light. Hereinafter, the points corresponding to each measurement direction of the point cloud data generated by the LIDAR 3 will also be referred to as "measurement points."

[0023] The sensor group 2 may include various external sensors and / or internal sensors in addition to the lidar 3. For example, the sensor group 2 may include a GPS (Global Positioning Satellite) receiver or the like required to generate position information.

[0024] (2) Configuration of information processing device 2 is a block diagram showing an example of the hardware configuration of the information processing device 1. The information processing device 1 mainly includes an interface 11, a memory 12, and a controller 13. These elements are connected to each other via a bus line.

[0025] The interface 11 performs interface operations related to the exchange of data between the information processing device 1 and an external device. In this embodiment, the interface 11 acquires output data from a sensor group 2 such as a lidar 3, and supplies the data to the controller 13. The interface 11 may be a wireless interface such as a network adapter for wireless communication, or may be a hardware interface for connecting to an external device via a cable or the like. The interface 11 may also perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0026] The memory 12 is configured by various types of volatile and non-volatile memory, such as a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk drive, and a flash memory. The memory 12 stores programs for the controller 13 to execute predetermined processes. The programs executed by the controller 13 may be stored in a storage medium other than the memory 12.

[0027] The controller 13 includes one or more processors such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a TPU (Tensor Processing Unit), and controls the entire information processing device 1. In this case, the controller 13 executes programs stored in the memory 12 or the like to perform various processes, which will be described later.

[0028] The controller 13 functions as an "acquisition means," a "calculation means," a "generation means," a computer that executes a program, and the like.

[0029] The processes executed by the controller 13 are not limited to being realized by software programs, but may be realized by any combination of hardware, firmware, and software. Furthermore, the processes executed by the controller 13 may be realized by using a user-programmable integrated circuit, such as an FPGA (Field-Programmable Gate Array) or a microcomputer. In this case, the programs executed by the controller 13 in this embodiment may be realized by using this integrated circuit.

[0030] (3) Generating stability information In summary, the controller 13 of the information processing device 1 generates a histogram of distances (also called a "distance histogram") for a predetermined time period at predetermined intervals for each measurement point of the lidar 3, and generates a history of distances to stationary objects based on the generated distance histogram. Then, the controller 13 generates stability information representing the stability of the stationary objects corresponding to the duration of existence of the stationary objects based on the history of distances to the stationary objects.

[0031] (3-1) Stability Considerations First, the concept (idea) of the stability calculated in this embodiment will be described. The stability indicated by the stability information represents the stability at which a stationary object is measured, and may be synonymous with the first stability described below, or may be a concept including both the first stability and the second stability described below.

[0032] In this embodiment, the information processing device 1, in principle, considers an object (or place) that remains stationary for a long period of time to have a higher stability as a stationary object (also referred to as "first stability"). In other words, the first stability represents the degree to which the object being measured exists stably as a stationary object. Furthermore, in addition to the above-mentioned first stability, the information processing device 1 may also consider an object to have a higher stability the fewer moving objects (i.e., objects crossing in front) that may block the laser light emitted by the lidar 3. The latter stability represents the degree to which measurements can be performed stably without the laser light being blocked by moving objects (so-called occlusion), and will hereinafter also be referred to as "second stability."

[0033] Fig. 3 is a diagram that schematically shows a space in which the lidar 3 performs measurements. In Fig. 3, the lidar 3 (lidar unit 100) is fixedly installed, and the space in which the lidar 3 performs measurements contains at least a wall 5, a structure 6 such as furniture, and a pedestrian 7. Furthermore, lines "L1" to "L3" each represent the optical path of the laser light corresponding to a certain measurement point, and "r00," "r10," "r20," and "r21," which correspond to points irradiated by the laser light on lines L1 to L3, represent the measurement distance to the corresponding irradiated point.

[0034] Here, at the measurement point corresponding to line L1, the distance r00 is measured almost all the time over a long period of time (for example, one year). In this case, it can be interpreted that a stationary object with a high first stability exists at the target measurement point. In addition, at the measurement point corresponding to line L1, no occlusion due to pedestrian movement or the like occurs, so the second stability is also high.

[0035] Furthermore, at the measurement point corresponding to line L2, distance r10 is measured over a long period of time (for example, one year), but because the laser light is irradiated on the pedestrian walkway at a height lower than the pedestrian, a distance shorter than distance r10 may be measured. In the example of FIG. 3, a distance shorter than distance r10 is measured because the laser light is irradiated on pedestrian 7. At this measurement point, the first stability is the same as that of the measurement point corresponding to line L1. On the other hand, the measurement point corresponding to line L2 is interpreted as having a lower second stability than the measurement point corresponding to line L1.

[0036] Furthermore, at the measurement point corresponding to line L3, a distance r20 was measured initially (for example, the first 10 months), and a distance r21 was measured over the period from then until the present (for example, 2 months). In this case, at the measurement point corresponding to line L3, it is considered that a stationary object is currently present at a position of distance r21. The first stability at the measurement point corresponding to line L3 is interpreted as being lower than the measurement points corresponding to lines L1 and L2 because the period during which the current stationary object was measured is short.

[0037] 4 is a table showing an example of the interpretation of the first stability. Here, "object observation period" indicates the length of the period during which the object has been continuously observed (or intermittently observed due to obstruction by a moving object) up to the present time, "expected situation" indicates the expected situation regarding whether the object to be measured is stationary or not, and "object existence probability at the next moment" indicates the probability of whether the object to be measured will still exist at the measurement time following the current one (i.e., the next measurement timing).

[0038] As shown in Figure 4, the longer the object observation period, the higher the probability of an object's presence at the next moment. The longer the object observation period, the more likely it is that an object is present that is less likely to move, and the shorter the object observation period, the more likely it is that an object is present that is more likely to move. The first stability has a positive correlation with the probability of an object's presence at the next moment, and the lower the probability of an object's presence at the next moment, the lower the first stability.

[0039] Next, a supplementary explanation will be given on the second stability.

[0040] The second stability depends on the frequency of occlusion. Generally, when a person passes in front of a stationary object (i.e., a position that blocks the laser light of the lidar 3), the distance measured is temporarily shorter than that of the stationary object. The frequency of occlusion depends on the location, time of day, etc., such as whether the area is crowded or sparsely populated.

[0041] For example, when measurements are taken by the lidar 3 for one year, consider a case in which the same distance is observed for 100% of the period, and a case in which the same distance is observed for 98% of the period and a different distance is observed for the remaining 2% of the period. In this case, the former case can be interpreted as having a higher second stability than the latter case. Also, depending on the frequency of occlusion occurrence, it may be effective to adjust the transmission frequency of the measured data, for example. Processing related to this idea will be explained in the second embodiment.

[0042] Next, a supplementary explanation will be given of the determination of the second stability when an object with a high first stability is temporarily blocked by another stationary object.

[0043] For example, an object (also referred to as a "second stationary object") may be temporarily placed in front of a first stationary object such as a wall with an extremely high first stability. In this case, while the second stationary object is placed, the second stationary object is measured at a stable distance, and the first stability for the second stationary object increases according to the time that has elapsed since the second stationary object was placed.

[0044] On the other hand, from the perspective of the first stationary object, the second stationary object placed in front of it is a "temporarily placed object (an object passing in front of it)", and it is highly likely that the first stationary object, such as a wall, will continue to exist in the same place even during the period when occlusion occurs due to the second stationary object placed in front of it.

[0045] Therefore, in the above example, even while a second stationary object is placed in front, distance information about the first stationary object such as a wall and information about the period of existence (measurement time) of the first stationary object must be retained in order to determine the first stability, and when the second stationary object that blocks the first stationary object moves and the first stationary object is observed again, the first stationary object must again be determined without delay to be an object with high stability as a stationary object. Calculation of stability taking into account the occurrence of this temporary occlusion will be described later with reference to FIG. 7(B).

[0046] (3-2) Creating distance histograms and determining distance to stationary objects The controller 13 generates a distance histogram at predetermined time intervals based on data measured by the lidar 3 for each measurement point over a predetermined period up to the present. In this case, the length of the period to be compiled in each distance histogram is determined, for example, to be the shortest length of time for which an object can be considered to be stationary. Specifically, the length of the period is set to an optimum value (for example, 10 seconds) determined based on experiments or the like so as to be the "shortest length of time for which an object can be considered to be stationary," and the optimum value is stored in advance in the memory 12 or the like. Hereinafter, the period to be compiled in each distance histogram will be referred to as the "histogram period," and the length of the period will also be referred to as the "histogram period length."

[0047] Here, a supplementary explanation will be given of the reason why the histogram period length is set as "the shortest time length for which an object can be considered to be stationary."

[0048] For example, even if the same distance is measured for a period shorter than the "shortest time period for which an object can be considered stationary" (e.g., one second), it may be the side of a passing object, and a stationary object may not have been measured. On the other hand, if the same distance is measured for the "shortest time period for which an object can be considered stationary," it is highly likely that the object is a stationary object placed there intentionally or a person or other moving object that has stopped temporarily. Taking the above into consideration, if the same distance is measured for the "shortest time period for which an object can be considered stationary," the likelihood that the object will still be present in the same location at the next measurement timing is higher than when the same distance is measured for a period shorter than the "shortest time period for which an object can be considered stationary." Note that, as in the second embodiment described below, when uploading measurement data of a stationary object by the LIDAR 3 to a server, if the same distance is measured continuously for the "shortest time period for which an object can be considered stationary," it is determined that a stationary object exists, and this can be applied to determining the data transmission frequency, etc.

[0049] The time interval for generating the distance histogram may be any time interval. For example, the distance histogram may be generated continuously for each histogram period length, or may be generated at a time interval shorter than the histogram period length while allowing for partial overlap of the histogram periods, or may be generated at a time interval longer than the histogram period length.

[0050] Preferably, the controller 13 generates measurement time information indicating the time (time period) when the distance corresponding to each bin (distance range considered to be the same in terms of frequency counting) of the distance histogram was measured together with each distance histogram. This measurement time information can be suitably used in determining the stability (especially the second stability).

[0051] Fig. 5 is a graph showing the relationship between the distance measured by the lidar 3 and time during a histogram period (here, 10 seconds) that is the target for generating a distance histogram at a certain measurement point. Fig. 6 is a diagram showing a distance histogram generated in the distance measurement situation shown in Fig. 5 and measurement time information that indicates the measurement time corresponding to each bin of the distance histogram.

[0052] The controller 13 generates the distance histogram shown in Fig. 6 by aggregating the distances measured by the LIDAR 3 based on a predetermined bin width. The controller 13 also generates measurement time information indicating the number of measurements for each bin based on the relationship between time and distance shown in Fig. 5. The measurement time is proportional to the number of occurrences and corresponds to the time obtained by multiplying the number of measurements by the measurement period (scanning period) of the LIDAR 3.

[0053] Next, the determination of the distance to a stationary object will be described. The controller 13 determines the distance to a stationary object based on a distance histogram such as that shown in FIG. 6. Specifically, the controller 13 extracts the peak distance in the distance histogram (i.e., the distance measured most frequently) as the distance to the stationary object. In the example of FIG. 6, the controller 13 extracts the distance "r0" as the peak distance. Then, the controller 13 determines that the distance r0 is the distance to the stationary object. This allows the controller 13 to suitably determine the distance to the stationary object.

[0054] Preferably, the controller 13 may further determine whether a peak distance occurs throughout the entire histogram period (from beginning to end) based on the measurement time information generated in association with the distance histogram, thereby determining whether a stationary object exists at the peak distance. In the examples of FIGS. 5 and 6, the peak distance r0 does not occur continuously throughout the histogram period, but occurs intermittently. Therefore, the controller 13 determines that a stationary object exists at the peak distance r0. Note that the controller 13 may also determine that a peak distance occurs throughout the entire histogram period, for example, if the number of occurrences at the peak distance is equal to or greater than a predetermined number stored in the memory 12 or the like. As in the examples of FIGS. 5 and 6, taking into account the occurrence of occlusion, the controller 13 may determine that the peak distance represents the distance of a stationary object, even if the peak distance occurs intermittently throughout the entire histogram period. This allows the controller 13 to more accurately determine the distance of a stationary object.

[0055] The controller 13 also calculates the farthest observed distance value included in the distance histogram. In this case, the controller 13 selects the longest distance from among distances measured with a frequency equal to or greater than a predetermined threshold (i.e., distances measured a predetermined number of times or more) as the farthest observed distance value, so as to exclude noise observed distance values. This farthest observed distance value is used in generating stability information, which will be described later.

[0056] (3-3) Generating stability information based on the distance history of stationary objects Next, the generation of stability information will be described. First, a method of generating stability information when only the first stability is taken into consideration will be described.

[0057] The controller 13 determines the distance to the stationary object and the farthest observed distance value for each distance histogram generated at predetermined intervals for each measurement point. As a result, the determination results of the distance to the stationary object and the farthest observed distance value in time series are obtained for each measurement point. Hereinafter, the determination results of the distance to the stationary object in time series are referred to as the "measured distance history," and the determination result of the farthest observed distance value in time series is also referred to as the "longest distance history."

[0058] Then, based on the measured distance history, the controller 13 calculates the length of time that the currently measured stationary object has been continuously present (also referred to as the "presence period length Tw"), and generates stability information that indicates stability corresponding to the presence period length Tw. In this case, based on the measured distance history, the controller 13 determines for each measurement point how far back in time the currently occurring stationary object has continued, and calculates the length of time going back in time as the presence period length Tw. Here, the stability indicated by the stability information may be the same as the presence period length Tw, or may be a value normalized so that the presence period length Tw falls within a predetermined value range.

[0059] FIG. 7(A) is a graph showing the measurement distance history at a certain measurement point. In the example of FIG. 7(A), a stationary object at distance "r1" has been present continuously for a time period "Tw1" from time "t1" to the present. Note that this shows that the distance of the stationary object determined for each histogram period has always been distance r1 from time t1 to the present. On the other hand, before time t1, a stationary object at distance r0, which is farther than distance r1, existed. Therefore, it cannot be considered that the object at distance r1 has been present continuously since before time t1.

[0060] From the above, in this case, the controller 13 determines that a stationary object at a distance r1 is present at the target measurement point, and that the duration Tw of its existence is the time length "Tw1" from time t1 to the present. Then, the controller 13 generates stability information representing the stability according to the time length Tw1 for the target measurement point.

[0061] Preferably, even if measurement of a stationary object with the same distance is temporarily interrupted, the controller 13 may consider the temporarily interrupted stationary object to be continuous if no object (not necessarily a stationary object) farther away (with a longer measured distance) than the stationary object was measured during the period in which measurement was interrupted. In this case, the controller 13 may consider the temporarily interrupted stationary object to be continuous if the longest distance history does not include a distance measured longer than the stationary object during the period in which measurement was interrupted. The controller 13 then calculates the existence period length Tw including the period in which measurement was interrupted. In this case, for example, the controller 13 may calculate the existence period length Tw including the period in which measurement was interrupted if the existence period length Tw calculated immediately before the temporary interruption of measurement is longer than the period in which measurement was interrupted by at least a predetermined multiple. The predetermined multiple may be set, for example, to an appropriate value stored in advance in the memory 12, etc. This allows the controller 13 to set a high level of stability in the stability information for stationary objects such as walls, even if a stably installed object that has existed for a long time is temporarily unable to be measured due to occlusion.

[0062] 7B is a graph showing another example of a measured distance history. In the example of FIG. 7B, a stationary object at distance r0 exists continuously from the present until time "t3." On the other hand, before time t3, a stationary object at distance r1 exists continuously from time t2 to time t3, and a stationary object at distance r0 exists continuously from time t1 to time t2. Thus, in FIG. 7B, measurement of the stationary object at distance r0 is temporarily interrupted by the presence of an object at distance r1.

[0063] In this case, the controller 13 compares the length of the period from time t2 when the measurement was temporarily interrupted to time t3 with the length of the period from time t1 when the distance r0 was measured immediately before the measurement was interrupted to time t2, and determines that the length of the latter period is longer than the length of the former period by at least a predetermined multiple. In this case, the controller 13 regards the length of time "Tw2" from time t1 to the present, which includes the period from time t2 when the measurement was temporarily interrupted to time t3, as the existence period Tw.

[0064] In this way, when a distance is measured intermittently, the controller 13 regards the length of the period of intermittent measurement as the existence period length Tw of the distance, based on the length of the period when the measurement of the distance stopped and the period before that when the distance was continuously measured. This enables the controller 13 to eliminate the influence of occlusion and set a high stability in the stability information of a stationary object with a high first stability.

[0065] As described above, the controller 13 can accurately calculate the presence period length Tw for each measurement point based on the measurement distance history and appropriately determine the stability for each measurement point. Then, the controller 13 generates stability information that represents a pair of the determined stability and the current measurement distance for each measurement point. The controller 13 generates the stability information at predetermined time intervals and stores the generated stability information in the memory 12, etc. Similarly, the controller 13 stores the measurement distance history for each measurement point in the memory 12, etc. so that it can be used for calculating the stability information next time.

[0066] (3-4) Generation of stability information taking into account the second stability Next, generation of stability information taking the second stability into consideration will be described. The controller 13 generates stability information by further using measurement time information generated in association with the distance histogram.

[0067] In this case, first, as a premise, when determining whether an object is stationary based on the distance histogram, the controller 13 calculates the occurrence frequency of the peak distance determined to be a stationary object (i.e., the proportion of the measurement time of the peak distance in the histogram period length). For example, the occurrence frequency (proportion) of the peak distance r0 in the distance histogram in Fig. 6 is about 60%.

[0068] In the first example of generating stability information, the controller 13 calculates the sum (also referred to as the "weighted sum") of values ​​obtained by multiplying each of the histogram period lengths constituting the existence period length Tw by the occurrence frequency (ratio) of the corresponding peak distance. This weighted sum corresponds to the existence period length Tw weighted based on the occurrence frequency of the target stationary object.

[0069] 7A, for example, the occurrence frequency of peak distance r1 is calculated for all histogram periods from time t1 to the present, and the sum of values ​​obtained by multiplying the calculated occurrence frequency by each histogram period length is calculated as a weighted total value. Then, controller 13 generates stability information in which the stability is the weighted total value or a value obtained by normalizing this to fall within a predetermined value range. This method allows controller 13 to suitably generate stability information that takes the second stability into consideration.

[0070] In a second example of generating stability information, the controller 13 calculates the second stability based on the occurrence frequency (ratio) of peak distances represented by measurement time information corresponding to each histogram period constituting the existence period, separately from the existence period length Tw. For example, in this case, the controller 13 calculates the second stability as the sum of the occurrence frequencies of peak distances in each histogram period constituting the target existence period or a normalized value of the sum. Then, the controller 13 calculates a stability that combines the first stability determined based on the existence period length Tw and the second stability based on the above-mentioned sum by statistical processing such as averaging (including weighted averaging), and generates stability information that represents the calculated stability. This method also allows the controller 13 to preferably generate stability information that combines the first stability and the second stability. Note that instead of generating stability information that combines the first stability and the second stability, the controller 13 may generate stability information that individually represents the first stability and the second stability.

[0071] Furthermore, the controller 13 may weight the histogram periods so as to emphasize time periods closer to the present when calculating the weighted total value. For example, the controller 13 sets a weight for each histogram period to be processed according to whether or not the period belongs to (or how close to) the current time period (e.g., time periods obtained by dividing a day into predetermined time periods). This weight is set to a higher value if the period belongs to (or is closer to) the current time period. Note that the controller 13 may further multiply this weight by a weight corresponding to the occurrence frequency of peak distances based on the measurement time information used in the first example of generating stability information described above. The controller 13 then calculates, as stability, the sum of values ​​obtained by multiplying the length of the histogram period by the weight set for each histogram period, or a value obtained by normalizing this sum.

[0072] By doing so, the controller 13 can preferably generate stability information that places emphasis on the distance measurement results in a situation close to the current situation.

[0073] (3-5) Processing flow FIG. 8 is an example of a flowchart showing the procedure of the stability information generation and update process executed by the information processing device 1 in the first embodiment.

[0074] First, the controller 13 of the information processing device 1 acquires point cloud data measured by the LIDAR 3 via the interface 11 and stores it in the memory 12 or the like (step S11). Then, the controller 13 determines whether it is time to generate a distance histogram (step S12). For example, the controller 13 recognizes the next histogram period to be generated based on a predetermined time interval for generating a distance histogram and a histogram period length, and determines that it is time to generate a distance histogram if point cloud data for that histogram period has already been acquired in step S11. Note that information regarding the time interval for generating a distance histogram and the histogram period length is stored in advance in the memory 12 or the like.

[0075] If it is time to generate a distance histogram (step S12; Yes), the controller 13 proceeds to step S13. On the other hand, if it is not time to generate a distance histogram (step S12; No), the controller 13 returns the process to step S11.

[0076] Next, when it is time to generate a distance histogram, the controller 13 generates a distance histogram corresponding to the target histogram period for each measurement point of the LIDAR 3, and determines the distance of a stationary object during that histogram period (step S13). When generating stability information taking the second stability into consideration, the controller 13 further generates measurement time information indicating the measurement time of the measured distance for each bin, in addition to the distance histogram.

[0077] Then, the controller 13 updates the measured distance history based on the distance determination result of the stationary object determined for each measurement point (step S14). As a result, the measured distance history to which the distance determination result of the stationary object corresponding to the latest histogram period calculated in step S13 has been added is stored in the memory 12, etc. Furthermore, when measurement time information is generated in step S13, the controller 13 adds the measurement time information to the measured distance history in association with the distance determination result of the stationary object. Note that the measurement time information added to the measured distance history does not need to be information representing the measurement time for each bin (see FIG. 6), but may be information regarding the measurement time of the peak distance determined to be the distance of a stationary object or the rate of measurement frequency based thereon.

[0078] Then, the controller 13 determines whether it is time to generate stability information (step S15). If it is time to generate stability information (step S15; Yes), the controller 13 generates stability information indicating the stability for each measurement point based on the measurement distance history stored in the memory 12 or the like, and stores the generated stability information in the memory 12 or the like (step S16).

[0079] Next, the controller 13 determines whether or not the processing should be terminated (step S17). For example, the controller 13 determines that the processing should be terminated when a predetermined condition is met, such as when the scanning of the LIDAR 3 is stopped or when an instruction to stop the processing by the controller 13 is detected. Then, when the controller 13 determines that the processing should be terminated (step S17; Yes), the controller 13 terminates the processing of the flowchart. On the other hand, when the controller 13 determines that the processing should be continued (step S17; No), the controller 13 returns the processing to step S11.

[0080] (4) Variations Next, a description will be given of preferred modifications of the first embodiment. The following modifications may be applied to the first embodiment described above in any combination.

[0081] (Variation 1) The controller 13 may generate stability information representing the second stability instead of the first stability.

[0082] In this case, for example, the controller 13 may execute the command "(3-4) Generation of stability information taking into account the second stability In this case, the controller 13 generates stability information representing the second stability calculated based on the second generation example described in the section "1. The stability information representing the second stability calculated based on the second generation example described in the section "2. The stability information representing the second stability calculated based on the second generation example described in the section "3. The stability information representing the second stability calculated based on the second generation example described in the section "4. The stability information representing the second stability calculated based on the second generation example described in the section "5. The stability information representing the second stability calculated based on the second generation example described in the section "6. The stability information representing the second stability calculated based on the second generation example described in the section "7. The stability information representing the second stability calculated based on the second generation example described in the section "8. The stability information representing the second stability calculated based on the second generation example described in the section "

[0083] In this case, instead of setting the histogram period length as "the shortest time length for which an object can be considered to be stationary" and setting the histogram period at predetermined intervals, the controller 13 may set the entire period (e.g., one year) considered in calculating the stability as one histogram period. In this case, the controller 13 calculates the occurrence frequency (ratio) of peak distances based on the distance histogram and measurement time information corresponding to one histogram period, and calculates the occurrence frequency or its normalized value as the second stability. Even when stability information is generated based on this modification, it is possible to generate stability information that is useful for various applications, such as transmission control of measurement data.

[0084] (Variation 2) The external sensor is not limited to the LIDAR 3, but may be another external sensor (such as a camera capable of measuring distance) capable of measuring distance (position in the depth direction). Even in this case, the information processing device 1 can suitably generate stability information based on the measurement results of the distance in time series for each measurement point measured by the external sensor.

[0085] As described above, the controller 13 of the information processing device 1 according to the first embodiment acquires time-series distance measurement results for each measurement point by the lidar 3, which is a measurement device that measures distance from a fixed position. Then, based on the acquired measurement results, the controller 13 calculates a distance histogram that indicates the measurement frequency of the distances measured for each measurement point. Then, based on the distance histogram, the controller 13 generates stability information that indicates the stability of measuring stationary objects for each measurement point. This allows the controller 13 to suitably generate stability information that is useful for, for example, controlling the transmission of measured data.

[0086] <Second Example> (1) Data collection system configuration 9 shows a schematic configuration of a data collection system in Example 2. The data collection system includes a lidar unit 100A and a data collection device 200. When the data collection device 200 collects and manages measurement data generated in the lidar unit 100A, the data collection system preferably reduces the amount of data transmitted from the lidar unit 100A to the data collection device 200.

[0087] The LIDAR unit 100A includes an information processing device 1A and a sensor group 2. Similar to the hardware configuration of the information processing device 1 in the first embodiment shown in FIG. 2, the information processing device 1A includes an interface 11, a memory 12, and a controller 13. The controller 13 of the information processing device 1A transmits data measured by the LIDAR 3 to the data collection device 200 via the interface 11 as upload information "Iu." In addition, in the process of transmitting the upload information Iu, the controller 13 of the information processing device 1A controls the frequency of data transmission for each measurement point of the LIDAR 3 based on the stability information. Similarly to the information processing device 1 in the first embodiment, the controller 13 of the information processing device 1A generates and updates the stability information, and stores the generated and updated stability information in the memory 12, etc. The controller 13 is an example of an "acquisition means," a "determination means," a "generation means," a "transmission frequency control means," a "transmission means," and a computer.

[0088] The data collection device 200 is a device that collects measurement data from a lidar, receives upload information Iu from the information processing device 1, and stores the received upload information Iu. Note that while only one set of lidar units 100A is shown in FIG. 1, multiple lidar units 100A may be present instead. In this case, the data collection device 200 receives upload information Iu from each lidar unit 100A.

[0089] (2) Upload information sending process Next, a description will be given of the process of transmitting uploaded information by the information processing device 1A. When a stationary object is measured by the LIDAR 3, the information processing device 1A determines whether transmission is necessary at the current timing for each measurement point so that the measured data is transmitted at a frequency according to the stability of the stationary object.

[0090] (2-1) Stationary object detection Here, a method for determining whether a stationary object has been measured will be described. For each measurement point, the information processing device 1A calculates the difference between the distance of a stationary object assumed to exist (also referred to as the "estimated distance") and the current (i.e., time of determination) measured distance, and compares this difference with a threshold (also referred to as the "stationary object determination threshold"). Then, if the difference between the assumed distance and the measured distance is equal to or less than the stationary object determination threshold, the information processing device 1A determines that the assumed stationary object has been measured. A method for setting the stationary object determination threshold will be described later. The assumed distance is the measured distance assumed for each measurement point, and may be the distance of a stationary object included in the stability information, or the distance measured one time before.

[0091] Next, a method for setting the stationary object determination threshold will be described. In the above-described first method for determining the stationary object determination threshold, the information processing device 1A sets the stationary object determination threshold to a fixed value stored in advance in the memory 12 or the like. In this case, the stationary object determination threshold may be a different value for each measurement point, or may be a common value for all measurement points. In the second method for determining the stationary object determination threshold, the information processing device 1A determines the stationary object determination threshold based on the fluctuation (variation) of the measurement distance of a stationary object assumed at the target measurement point. In the third method for determining the stationary object determination threshold, the information processing device 1A determines the stationary object determination threshold based on the stability indicated by the stability information. Note that, as will be described later, the information processing device 1A may determine the stationary object determination threshold by combining the second and third determination methods.

[0092] Here, the second method for determining the stationary object determination threshold will be specifically described. Fig. 10(A) is a graph showing the change over time in the measured distance at a certain measurement point. This change over time in the measured distance indicates the transition of the measured distance during a period during which a stationary object assumed at the target measurement point is measured (for example, a period corresponding to the presence period length Tw). Fig. 10(B) is a histogram showing the results of frequency aggregation of the measured distances in Fig. 10(A). Furthermore, dashed line 80 indicates the distance (representative distance) of a stationary object included in the stability information. Arrow 81 indicates the range of measured distances within which a predetermined proportion (for example, 95%) of the measured distances are distributed.

[0093] In this case, the information processing device 1A calculates the variance of the measured distances during the period in which a stationary object assumed to be at the target measurement point was measured in the past, and sets the stationary object determination threshold according to the variance. In this case, the information processing device 1A sets a larger stationary object determination threshold based on a predetermined formula or the like, as the variance increases. As a result, the information processing device 1A suitably sets the stationary object determination threshold so that the range in which an object is determined to be a stationary object using the stationary object determination threshold falls within the range indicated by arrow 81 (i.e., the range in which almost all past measured distances for the target stationary object fall).

[0094] Next, a third method for determining the stationary object determination threshold will be specifically described. For example, based on a predetermined formula, the information processing device 1A increases the stationary object determination threshold as the stability indicated by the stability information increases. In this way, the information processing device 1A changes the stationary object determination threshold in a direction that makes it easier to determine a stationary object as the stability increases. This allows the information processing device 1A to effectively prevent unnecessary uploading of measurement results corresponding to measurement points with high stability to the data collecting device 200 due to measurement errors or the like. In this case, the information processing device 1A preferably determines the stationary object determination threshold based on the second stability. In this case, the information processing device 1A sets the stationary object determination threshold in a direction that makes it easier to determine a stationary object for measurement points where occlusions or the like are unlikely to occur. This allows the information processing device 1A to effectively prevent unnecessary uploading of measurement results corresponding to measurement points where distances are stably measured to the data collecting device 200.

[0095] The information processing device 1A may determine the stationary object determination threshold by combining the second determination method and the third determination method. In this case, the information processing device 1A determines the stationary object determination threshold by referring to a predetermined formula or a lookup table, for example, based on the variance calculated in the second determination method and the stability used in the third determination method.

[0096] (2-2) Transmission Timing Next, the timing of transmitting the upload information Iu will be specifically described. The information processing device 1A compares the difference between the expected distance and the currently measured distance for each measurement point with a stationary object determination threshold, and immediately transmits the current measurement data at the measurement point where the difference becomes larger than the stationary object determination threshold to the data collection device 200 as upload information Iu. In this case, the information processing device 1A determines that an object different from the expected stationary object has been measured at the target measurement point, and suitably uploads the latest measurement results of the LIDAR 3 to the data collection device 200. As a result, the information processing device 1A can limit the transmission of measurement results for measurement points that differ from the predicted measurement results, thereby suitably suppressing the amount of data.

[0097] Furthermore, the information processing device 1A transmits measurement data for measurement points where the difference between the expected distance and the currently measured distance is equal to or less than the stationary object determination threshold to the data collecting device 200 at a transmission frequency (transmission interval) determined based on the stability indicated by the stability information. In this case, a formula or lookup table that defines the relationship between the stability and the transmission frequency is stored in advance in the memory 12 or the like, and the information processing device 1A determines the transmission frequency by referring to this formula or the like. For example, the information processing device 1A transmits measurement data from measurement points where the stability is equivalent to an existence period length Tw of one hour only once every 10 minutes, transmits measurement data from measurement points where the stability is equivalent to an existence period length Tw of one day only once every hour, and transmits measurement data from measurement points where the stability is equivalent to an existence period length Tw of one second every frame.

[0098] In these cases, the information processing device 1A transmits, as upload information Iu, measurement results corresponding to measurement points where the time interval from the immediately preceding transmission timing to the present is equal to or greater than the time interval corresponding to the transmission frequency determined based on the stability, to the data collecting device 200. This allows the information processing device 1A to upload the measurement results at each measurement point to the data collecting device 200 at an appropriate frequency according to the stability.

[0099] (2-3) Processing flow Fig. 11 is an example of a flowchart showing the procedure of upload processing executed by information processing device 1A. Note that information processing device 1A may execute the upload processing shown in Fig. 11 in parallel with the flowchart of stability information generation / update processing shown in Fig. 8.

[0100] First, the information processing device 1A acquires point cloud data including the measurement results of each measurement point measured by the lidar 3 in one scan (step S21).

[0101] Then, the information processing device 1A calculates, for each measurement point, the difference between the estimated distance based on the stability information, etc. and the measured distance based on the point cloud data acquired in step S21 (step S22). Furthermore, the information processing device 1A determines a stationary object determination threshold for each measurement point (step S23). In this case, the information processing device 1A may set the stationary object determination threshold to a value stored in a memory, etc., or may adaptively determine the stationary object determination threshold based on the variance and / or stability of the measured distance.

[0102] Then, the information processing device 1A determines whether or not there is a measurement point where the difference calculated in step S22 is greater than the stationary object determination threshold determined in step S23 (step S24). If there is a measurement point where the difference is greater than the stationary object determination threshold (step S24; Yes), the information processing device 1A determines that an object other than the expected stationary object has been detected at that measurement point, and immediately uploads the measurement data of the corresponding measurement point as upload information Iu to the data collecting device 200 (step S25). Furthermore, the information processing device 1A executes the processes of steps S27 and S28, which will be described later, for the measurement data of the measurement point where the difference is equal to or less than the stationary object determination threshold (step S26). As a result, the information processing device 1A uploads the measurement data of the measurement result of the measurement point where the expected stationary object has been detected to the data collecting device 200 as upload information Iu at a transmission frequency based on the stability.

[0103] On the other hand, if there is no measurement point where the difference is greater than the stationary object determination threshold (step S24; No), the information processing device 1A determines the transmission frequency for each measurement point based on the stability for each measurement point represented by the stability information (step S27). Then, the information processing device 1A uploads the measurement results at the measurement points whose transmission timing is based on the transmission frequency determined in step S27 as upload information Iu to the data collecting device 200 (step S28). Note that the information processing device 1A may transmit the upload information Iu compressed by any lossless or lossy compression to the data collecting device 200.

[0104] Next, the controller 13 determines whether or not the processing should be terminated (step S29). For example, the controller 13 determines that the processing should be terminated when a predetermined condition is met, such as when the scanning of the LIDAR 3 is stopped or when an instruction to stop the processing by the controller 13 is detected. Then, when the controller 13 determines that the processing should be terminated (step S29; Yes), the controller 13 terminates the processing of the flowchart. On the other hand, when the controller 13 determines that the processing should be continued (step S29; No), the controller 13 returns the processing to step S21. (3) Variations Next, a description will be given of preferred modifications of the second embodiment. The following modifications may be applied to the second embodiment described above in any combination.

[0105] (Variation 1) Instead of performing the process of generating and updating stability information corresponding to the first embodiment, the information processing device 1A may store previously generated stability information in the memory 12 or the like. In this case, the stability information includes, for example, information regarding the distance to and stability of a stationary object that is assumed to be measured within the measurement range of the LIDAR 3. Even when referring to such stability information, the information processing device 1A can preferably determine the frequency of transmitting measurement data for each measurement point of a stationary object and the stationary object determination threshold used for stationary object determination, based on the stability for each measurement point represented by the stability information.

[0106] (Variation 2) The LIDAR unit 100A may include, instead of the LIDAR 3, an external sensor that measures distance other than the LIDAR 3, or a camera that does not measure distance.

[0107] For example, if the lidar unit 100A is equipped with a camera that cannot measure distance, stability information indicating the stability of measuring a stationary object for each measurement point corresponding to each pixel is stored in the memory 12 or the like. Then, the information processing device 1A detects an image area where a stationary object exists based on the image generated by the camera using a known object recognition technique or the like, and determines the data transmission frequency for pixels (measurement points) where a stationary object is determined to exist based on the stability for each measurement point indicated by the stability information. Meanwhile, the information processing device 1A immediately transmits data of areas other than the image area where a stationary object exists to the data collecting device 200 as upload information Iu. In this way, according to this modification, the amount of data to be transmitted can be suitably reduced even when transmitting measurement results from a measuring device other than the lidar 3 to the data collecting device 200.

[0108] As described above, the controller 13 of the information processing device 1 according to this embodiment acquires measurement results for each measurement direction by the lidar 3, which is a measurement device that measures from a fixed position. Then, based on the acquired measurement results, the controller 13 determines for each measurement point whether an expected stationary object has been measured. Then, the controller 13 determines the transmission frequency for transmitting the measurement results for a measurement point where it has been determined that a stationary object has been measured to the data collecting device 200, based on the stability at that measurement point. In this way, the controller 13 can accurately determine the transmission frequency for measurement results corresponding to measurement points where expected stationary objects have been measured, and can suitably suppress an increase in the data volume of the uploaded information Iu.

[0109] In the above-described embodiments, the program can be stored using various types of non-transitory computer-readable media and supplied to a controller or the like that is a computer. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)).

[0110] Although the present invention has been described above with reference to the examples, the present invention is not limited to the above examples. Various modifications within the scope of the present invention that would be understood by those skilled in the art can be made to the configuration and details of the present invention. In other words, the present invention naturally includes various modifications and alterations that would be possible for those skilled in the art in accordance with the entire disclosure, including the claims, and the technical ideas. Furthermore, the disclosures of the above-cited patent documents and other documents are incorporated herein by reference. [Explanation of symbols]

[0111] 1, 1A Information processing equipment 2 Sensor group 3 Rider 100, 100A Lidar Unit 200 Data Collection Device

Claims

1. an acquisition means for acquiring a measurement result for each measurement point corresponding to a measurement direction by a measurement device that measures from a fixed position; a determination means for determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; a transmission frequency control means for determining a transmission frequency for transmitting the measurement results for the measurement point at which it is determined that the stationary object has been measured to a data collection device based on the stability of the measurement of the stationary object at the measurement point; An information processing device having the above.

2. 2. The information processing device according to claim 1, further comprising a transmitting means for transmitting to the data collecting device the measurement results for the measurement points where it is determined that the stationary object has been measured based on the transmission frequency, and for immediately transmitting to the data collecting device the measurement results for the measurement points where it is determined that the stationary object has not been measured.

3. The measurement device measures the distance at each measurement point, 3. The information processing device according to claim 1, wherein the determination means determines whether the stationary object has been measured for each measurement point based on a difference between a measured distance measured by the measuring device and an estimated distance of the stationary object.

4. The information processing apparatus according to claim 3 , wherein the determining means determines whether the stationary object has been measured for each measurement point based on the difference and a threshold value set based on an index of variation in the past measured distances.

5. The information processing apparatus according to claim 3 , wherein the determining means determines whether or not the stationary object has been measured for each of the measurement points based on the difference and a threshold value set based on the stability.

6. The information processing device according to any one of claims 1 to 5, wherein the transmission frequency control means determines the transmission frequency based on the stability identified by referring to stability information indicating at least the stability for each measurement point.

7. The information processing apparatus according to claim 6 , further comprising: a generating unit that generates the stability information based on the time-series measurement results for each of the measurement points obtained by the measurement device.

8. The information processing device described in any one of claims 1 to 7, wherein the stability is a first stability representing the degree to which the stationary object exists stably as a stationary object, or a second stability representing the degree to which the stationary object can be measured stably without being obstructed by other objects, or an index that combines the first stability and the second stability.

9. Obtaining measurement results for each measurement point corresponding to the measurement direction by a measurement device that measures from a fixed position; determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; determining a frequency of transmitting the measurement results for the measurement point at which it is determined that the stationary object has been measured to a data collection device based on the stability of the measurement of the stationary object at the measurement point; Information processing methods.

10. Obtaining measurement results for each measurement point corresponding to the measurement direction by a measurement device that measures from a fixed position; determining whether or not an expected stationary object has been measured for each measurement point based on the measurement results; A program that causes a computer to execute a process of determining the frequency with which measurement results for the measurement point at which it is determined that the stationary object has been measured are transmitted to a data collection device based on the stability with which the stationary object is being measured at the measurement point.

11. A storage medium storing the program according to claim 10.

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