Detection system, detection method, and detection program

Abstract

To provide a detection system, a detection method, and a detection program which are capable of accurately detecting stain of a distance measuring part.SOLUTION: An acquisition processing part 211 acquires a plurality of distance measurement data measured by a sensor 24 which measures a distance to a subject in a predetermined distance measuring direction. A calculation processing part 212 calculates variation of the plurality of distance measurement data acquired by the acquisition processing part 211. A determination processing part 213 determines the state of stain of the sensor 24 on the basis of the variation calculated by the calculation processing part 212.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a technique for detecting dirt on a distance measuring unit (distance measuring sensor). [Background technology]

[0002] Conventionally, there are known autonomous driving devices (autonomous vehicles, AGVs, etc.) capable of autonomous driving while detecting and avoiding obstacles. In addition, a technology is known for detecting dirt such as foreign matter adhering to an optical window in a distance measuring unit (e.g., a lidar sensor) mounted on the autonomous driving device and detecting obstacles (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-70796 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, if large pieces of dust or dirt adhere to the optical window of the distance measuring unit, the value of the detection data of the distance measuring unit in the dirty area changes significantly, making it possible to detect dirt on the distance measuring unit. However, if, for example, a very small particle of dust adheres to the optical window, the detection data of the distance measuring unit will be sometimes normal and sometimes abnormal, resulting in unstable detection data being obtained, making it difficult to accurately detect dirt.

[0005] An object of the present disclosure is to provide a detection system, a detection method, and a detection program capable of accurately detecting dirt on a distance measuring unit. [Means for solving the problem]

[0006] A detection system according to one aspect of the present disclosure includes an acquisition processing unit, a calculation processing unit, and a determination processing unit. The acquisition processing unit acquires a plurality of distance measurement data measured by a distance measurement unit that measures a distance to an object in a predetermined distance measurement direction. The calculation processing unit calculates a variance of the plurality of distance measurement data acquired by the acquisition processing unit. The determination processing unit determines a dirt state of the distance measurement unit based on the variance calculated by the calculation processing unit.

[0007] A detection method according to another aspect of the present disclosure is a method in which one or more processors execute the following steps: acquire multiple distance measurement data measured by a distance measurement unit that measures the distance to an object in a specified distance measurement direction, calculate the variance of the multiple distance measurement data, and determine the state of dirt on the distance measurement unit based on the variance.

[0008] A detection program relating to another aspect of the present disclosure is a program for causing one or more processors to execute the following operations: acquiring multiple distance measurement data measured by a distance measurement unit that measures the distance to an object in a specified distance measurement direction, calculating the variance of the multiple distance measurement data, and determining the state of dirt on the distance measurement unit based on the variance. Effect of the Invention

[0009] According to the present disclosure, it is possible to provide a detection system, a detection method, and a detection program capable of accurately detecting dirt on a distance measuring unit. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing an overall configuration of an autonomous driving device according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a perspective view showing the appearance of the autonomous driving device according to an embodiment of the present disclosure. [Diagram 3] FIG. 3 is a diagram illustrating an example of a detectable range of the distance measuring device according to the embodiment of the present disclosure. [Figure 4A]FIG. 4A is a graph showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 4B] FIG. 4B is a graph showing an example of a measurement result of the sensor according to an embodiment of the present disclosure. [Figure 5A] FIG. 5A is a histogram showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 5B] FIG. 5B is a histogram showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 6A] FIG. 6A is a graph showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 6B] FIG. 6B is a histogram showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram illustrating an example of distance measurement by a sensor according to an embodiment of the present disclosure. [Figure 8] FIG. 8 is a diagram illustrating an example of distance measurement by the sensor according to an embodiment of the present disclosure. [Figure 9] FIG. 9 is a flowchart illustrating an example of a procedure of a dirt detection process executed in the distance measuring device according to the embodiment of the present disclosure. [Figure 10] FIG. 10 is a diagram showing a map of an area in which an autonomous driving device according to an embodiment of the present disclosure travels. [Figure 11A] FIG. 11A is a graph showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 11B] FIG. 11B is a graph showing an example of a measurement result of the sensor according to an embodiment of the present disclosure. [Figure 12A] FIG. 12A is a histogram showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. [Figure 12B] FIG. 12B is a histogram showing an example of a measurement result of a sensor according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Note that the following embodiments are examples of the present disclosure and are not intended to limit the technical scope of the present disclosure.

[0012] [Autonomous Driving Device 1] Fig. 1 is a block diagram showing a configuration of an autonomous driving device 1 according to an embodiment of the present disclosure, and Fig. 2 is a perspective view showing an external appearance of the autonomous driving device 1. The autonomous driving device 1 is an autonomously-driving type mobile body (also called an AGV) capable of driving a preset driving route while estimating its own position. The autonomous driving device 1 is configured to be capable of data communication with a server (not shown), and autonomously drives a driving route according to driving instructions acquired from the server.

[0013] The autonomous mobile device 1 includes driving wheels provided on the bottom of the main body, driven wheels rotatably provided on the bottom of the main body, a distance measuring device 2 that measures the distance to objects around the autonomous mobile device 1, and a battery (not shown) that supplies power to the main body. The autonomous mobile device 1 autonomously travels while measuring the distance to an object (target object) based on detection data (distance measurement data) by the distance measuring device 2, and if it determines that the object is an obstacle, it avoids the obstacle or stops traveling.

[0014] 1, the autonomous mobile device 1 includes a control unit 11, a storage unit 12, a communication unit 13, and a distance measuring device 2. The autonomous mobile device 1 and the distance measuring device 2 are connected to each other wirelessly or via a wire so as to be able to communicate data with each other.

[0015] The communication unit 13 is a communication interface for connecting the autonomous mobile device 1 to a network wirelessly or by infrared light, and for executing data communication with the server via the network in accordance with a predetermined communication protocol.

[0016] The storage unit 12 is a non-volatile storage unit such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory that stores various information. Specifically, the storage unit 12 stores route data received from the server.

[0017] The storage unit 12 also stores a control program for causing the control unit 11 to execute various processes. For example, the control program is non-temporarily recorded on a computer-readable recording medium such as a CD or a DVD, and is read by a reading device (not shown) such as a CD drive or a DVD drive provided in the autonomous traveling device 1 and stored in the storage unit 12.

[0018] The control unit 11 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various arithmetic processes. The ROM is a non-volatile storage unit in which control programs such as a BIOS and an OS for causing the CPU to execute various arithmetic processes are stored in advance. The RAM is a volatile or non-volatile storage unit that stores various information, and is used as a temporary storage memory (work area) for various processes executed by the CPU. The control unit 11 controls the autonomous driving device 1 by executing, by the CPU, various control programs stored in advance in the ROM or the storage unit 12.

[0019] Specifically, the control unit 11 includes various processing units such as a driving processing unit 111. The control unit 11 functions as the various processing units by executing various processes according to the control program with the CPU. Some or all of the processing units may be configured with electronic circuits. The control program may be a program for causing a plurality of processors to function as the processing units.

[0020] The driving processing unit 111 drives the autonomous driving device 1. For example, the driving processing unit 111 drives the autonomous driving device 1 along a preset driving route while estimating its own position on a map by a well-known self-position estimation method.

[0021] Here, a general method of estimating the self-position of the autonomous mobile device 1 will be described. Here, an example is taken of a configuration in which translation and turning are achieved by the speed difference between the left and right drive wheels. The autonomous mobile device 1 moves straight by rotating the left and right drive wheels in the same direction, and turns on the spot (stationary turning) by rotating them in opposite directions. The autonomous mobile device 1 can also travel in an arc by creating a speed difference between the left and right drive wheels. The autonomous mobile device 1 is equipped with left and right drive wheels, and an encoder (not shown) for measuring the rotation angle of each drive wheel.

[0022] First, the current attitude (initial position) of the autonomous mobile device 1 on a map corresponding to the travel area is set. This setting process may be performed by an operator on an operation terminal, or, in the case where travel starts from a charging station, it may be performed automatically by determining the position of the charging station on the map in advance.

[0023] The autonomous mobile device 1 translates at the average speed of each drive wheel and turns at the speed difference. The translation speed and turning speed can be calculated from the rotation angle per time observed by the encoder, the radius of the drive wheels, and the distance between the left and right drive wheels. In addition, the travel trajectory of the autonomous mobile device 1 on the map can be calculated by integrating the translation speed and turning speed.

[0024] Here, the speed obtained from the observed value of the encoder contains errors. These are caused by errors in the radius of the driving wheels, errors in the distance between the left and right driving wheels, slippage of the driving wheels, etc. For this reason, the calculated trajectory of the autonomous mobile device 1 on the map and the actual trajectory of the autonomous mobile device 1 gradually deviate from each other.

[0025] As the autonomous driving device 1 continues to drive, the positional deviation increases and eventually its own position becomes unknown. Therefore, the autonomous driving device 1 executes a correction process to correct its own position using the measurement results of the distance measuring device 2.

[0026] If the autonomous vehicle's position on the map is correct, the location of the surrounding objects measured by the distance measuring device 2 will match the location of the objects on the map. If the measured location of the object does not match the location of the object on the map, the autonomous vehicle 1 corrects the trajectory on the map every time it moves a certain distance or a certain turning angle so that the location of the object measured by the distance measuring device 2 matches the object on the map. A specific method known is the Monte Carlo method.

[0027] In this manner, the autonomous mobile device 1 travels along a preset travel route by estimating its own position by matching the objects on the map while the distance measuring device 2 measures (detects) surrounding objects.

[0028] Next, the configuration of the distance measuring device 2 will be described. As shown in Fig. 1, the distance measuring device 2 includes a control unit 21, a storage unit 22, a communication unit 23, a sensor 24, etc. As shown in Fig. 2, the distance measuring device 2 is provided on the upper front part of the autonomous mobile device 1. One distance measuring device 2 may be provided on the front, or two may be provided on the front and rear. Four distance measuring devices 2 may be provided on the front, rear, left and right sides.

[0029] The communication unit 23 is a communication interface for connecting the distance measuring device 2 to a network by wire or wirelessly and executing data communication between the autonomous mobile device 1 and the server via the network in accordance with a predetermined communication protocol. For example, the communication unit 23 is capable of data communication with the communication unit 13 of the autonomous mobile device 1 and transmits distance measurement data of the distance measuring device 2 (sensor 24).

[0030] The storage unit 22 is a non-volatile storage unit such as an HDD, an SSD, or a flash memory that stores various types of information. Specifically, the storage unit 22 stores the distance measurement data and the like.

[0031] The storage unit 22 also stores control programs such as a dirt detection program for causing the control unit 21 to execute a dirt detection process (see FIG. 9) described below. For example, the dirt detection program is non-temporarily recorded on a computer-readable recording medium such as a CD or DVD, and is read by a reading device (not shown) such as a CD drive or DVD drive provided in the distance measuring device 2 and stored in the storage unit 22. The dirt detection program is an example of a detection program of the present disclosure.

[0032] The sensor 24 is a distance sensor (e.g., a lidar sensor) capable of measuring the distance to an object using laser light. The sensor 24 is an example of a distance measuring unit of the present disclosure.

[0033] Specifically, the sensor 24 uses a mirror and a MEMS (Micro Electro Mechanical Systems) to irradiate the surroundings with laser light, receives the reflected light, and measures the time difference between irradiation and reception to measure the distance to an object in the direction of laser irradiation. At this time, by repeating the direction of irradiation of the laser light in a certain pattern, the arrangement of objects in space can be observed at a frequency of several tens of Hz. For example, the sensor 24 measures the distance to an object by irradiating (scanning) the range (detectable range Ar) shown in FIG. 3 with laser light. The sensor 24 sequentially scans the detectable range Ar (scanning range) with the laser light at a predetermined scanning interval. FIG. 3 shows the state in which the sensor 24 irradiates the laser light in a predetermined scanning direction (angle D1). The sensor 24 stores data (distance measurement data (distance L1)) measured by scanning at the angle D1 in the memory unit 22. In addition, the sensor 24 irradiates the laser light multiple times at the angle D1, and therefore stores multiple distance measurement data corresponding to the angle D1 in the memory unit 22. The sensor 24 measures the distance in each scanning direction while changing the scanning direction within the detectable range Ar.

[0034] The control unit 21 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various arithmetic processes. The ROM is a non-volatile storage unit in which control programs such as a BIOS and an OS for causing the CPU to execute various arithmetic processes are stored in advance. The RAM is a volatile or non-volatile storage unit that stores various information, and is used as a temporary storage memory (work area) for various processes executed by the CPU. The control unit 21 controls the distance measuring device 2 by executing various control programs stored in advance in the ROM or the storage unit 22 by the CPU.

[0035] Specifically, as shown in Fig. 1, the control unit 21 includes various processing units such as an acquisition processing unit 211, a calculation processing unit 212, a determination processing unit 213, and a countermeasure processing unit 214. The control unit 21 functions as the various processing units by executing various processes according to the dirt detection program with the CPU. Also, a part or all of the processing units may be configured with electronic circuits. Also, the dirt detection program may be a program for causing a plurality of processors to function as the processing units.

[0036] The acquisition processing unit 211 acquires multiple distance measurement data by the sensor 24 that measures the distance to an object (target) in a predetermined distance measurement direction (scanning direction). For example, in the example shown in Fig. 3, the acquisition processing unit 211 acquires multiple distance measurement data measured by the sensor 24 irradiating (scanning) a laser light in the direction of angle D1. The acquisition processing unit 211 also acquires multiple distance measurement data corresponding to the direction of angle D1 stored in the storage unit 22.

[0037] The acquisition processor 211 also plots the acquired multiple distance measurement data on a graph showing the relationship between time and distance. An example of the graph is shown in Fig. 4A and Fig. 4B. Fig. 4A shows an example of a case where there is no dirt at a position in a predetermined scanning direction (angle D1 in this case) of the sensor 24, and Fig. 4B shows an example of a case where there is minute dirt at a position in the predetermined scanning direction (angle D1) of the sensor 24. When there is no dirt at the position of angle D1 of the sensor 24, a constant distance (distance L1 in this case) is measured for each of the multiple scans, as shown in Fig. 4A.

[0038] In contrast, when there is a tiny stain at the angle D1 position of the sensor 24, as shown in Figure 4B, among multiple scans, there are cases where the correct distance (distance L1) is measured (plotted by a white circle in Figure 4B) and cases where an incorrect distance (less than distance L1) is measured (plotted by a black circle in Figure 4B), resulting in variation in the measurement results.

[0039] The calculation processing unit 212 calculates the variance of the multiple distance measurement data acquired by the acquisition processing unit 211. Specifically, the calculation processing unit 212 calculates the variance of the multiple distance measurement data measured in a predetermined scanning direction. For example, the calculation processing unit 212 calculates the average value of the multiple distance measurement data (distance) measured in the scanning direction of angle D1, and calculates the variance (degree of variance) by squaring the differences between each distance measurement data and the average value and dividing the sum by the number of distance measurement data. The variance is, for example, "variance", but is not limited to this, and standard deviation, coefficient of variation, etc. may be used. Below, the variance will be described as an example of the variance.

[0040] Here, histograms of the measurement results shown in Figures 4A and 4B are shown in Figures 5A and 5B. When a constant distance (distance L1) is measured for each of multiple scans in a predetermined scanning direction (see Figure 4A), the frequency of occurrence of the distance measurement data is concentrated (a peak appears) at the position of distance L1, as shown in Figure 5A. In contrast, when the measured distance varies for each of multiple scans in a predetermined scanning direction (see Figure 4B), the frequency of occurrence of the distance measurement data appears not only at the position of distance L1 but also at other positions, as shown in Figure 5B.

[0041] For this reason, for example, in the measurement results shown in Figures 4A and 5A, the variance calculated by calculation processing unit 212 is a small value. In contrast, for example, in the measurement results shown in Figures 4B and 5B, the variance calculated by calculation processing unit 212 is a large value.

[0042] The determination processing unit 213 determines the contamination state of the sensor 24 based on the variance calculated by the calculation processing unit 212. Specifically, when the variance calculated by the calculation processing unit 212 is equal to or greater than a threshold, the determination processing unit 213 determines that the sensor 24 is contaminated, and when the variance is less than the threshold, the determination processing unit 213 determines that the sensor 24 is not contaminated. When the variance is less than the threshold in the measurement results shown in Figures 4A and 5A, the determination processing unit 213 determines that the sensor 24 is not contaminated. On the other hand, when the variance is equal to or greater than the threshold in the measurement results shown in Figures 4B and 5B, the determination processing unit 213 determines that the sensor 24 is contaminated.

[0043] This makes it possible to detect, for example, minute stains that cause variation in the measurement results when they are attached to the sensor 24. When large stains are attached to the sensor 24, a peak in the distance measurement data appears at a position (distance L0) closer than the actual distance L1 as shown in Figures 6A and 6B, and the determination processing unit 213 can determine that large stains are attached based on this result. In this way, the determination processing unit 213 can determine the presence or absence of stains and the degree of stains (minute stains, large stains, etc.) based on the measurement results (distance, appearance frequency, variance).

[0044] In the above example, an example of determining the presence or absence of dirt in a predetermined scanning direction (angle D1) has been shown, but the control unit 21 determines the presence or absence of dirt for each of a plurality of scanning directions (distance measurement directions). Specifically, the acquisition processing unit 211 acquires a plurality of distance measurement data for each of a plurality of scanning directions, and the calculation processing unit 212 calculates the variance for each of the plurality of scanning directions. Then, the determination processing unit 213 determines the state of dirt for each of the plurality of distance measurement directions based on each variance.

[0045] The corrective action processor 214 executes a process (corrective action) according to the result of the determination made by the determination processor 213.

[0046] For example, when the range of the scanning direction in which dirt is detected is less than the first range among the multiple scanning directions, the countermeasure processing unit 214 stops the determination processing of the determination processing unit 213 in the range and allows the autonomous mobile device 1 to continue traveling. For example, when dirt is detected in 1 to 5 scanning directions among the multiple scanning directions, the countermeasure processing unit 214 filters the distance measurement data in the scanning direction and ignores object detection in the scanning direction. The countermeasure processing unit 214 also outputs an instruction to the control unit 11 to continue traveling of the autonomous mobile device 1, thereby allowing the autonomous mobile device 1 to continue traveling.

[0047] Furthermore, for example, when the range of the scanning direction in which the dirt is detected is equal to or larger than the first range and smaller than the second range among the multiple scanning directions, the countermeasure processing unit 214 stops the determination processing of the determination processing unit 213 in the range and decelerates the autonomous mobile device 1. For example, when dirt is detected in 6 to 10 scanning directions among the multiple scanning directions, the countermeasure processing unit 214 cancels the distance measurement data for the scanning direction. Furthermore, the countermeasure processing unit 214 outputs an instruction to the control unit 11 to decelerate the autonomous mobile device 1, thereby reducing the traveling speed.

[0048] Furthermore, for example, when the range of the scanning directions in which dirt is detected is equal to or greater than the second range among the multiple scanning directions, the countermeasure processing unit 214 stops the autonomous mobile device 1. For example, when dirt is detected in more than 10 scanning directions among the multiple scanning directions, the countermeasure processing unit 214 outputs an instruction to the control unit 11 to stop the autonomous mobile device 1, thereby halting the autonomous traveling. Furthermore, the countermeasure processing unit 214 notifies the user of predetermined information. For example, the countermeasure processing unit 214 notifies the user of a message urging the user to clean the sensor 24.

[0049] In this way, the countermeasure processing unit 214 may control the traveling of the autonomous mobile device 1 based on the degree of dirtiness determined by the determination processing unit 213.

[0050] In another embodiment, the countermeasure processing unit 214 may notify the user of a message according to the degree of dirtiness of the sensor 24. For example, as the degree of dirtiness increases, the variance calculated by the calculation processing unit 212 increases, so the countermeasure processing unit 214 notifies the user of a message encouraging cleaning when the calculated variance exceeds a threshold. Also, since there is a correlation between the change in variance and the path of time, for example, the countermeasure processing unit 214 may predict the time when cleaning will be necessary and notify the user of the predicted time.

[0051] Here, in order to accurately determine the dirt state of the sensor 24, the distance measuring device 2 may have the following configuration.

[0052] For example, when the distance measuring device 2 is installed in the autonomous mobile device 1, on the condition that the autonomous mobile device 1 is stopped, the calculation processing unit 212 calculates the variance and the determination processing unit 213 determines the dirt state of the sensor 24. This fixes the distance (e.g., distance L1 in FIG. 3) between the sensor 24 and the object (obstacle), making it possible to accurately determine the dirt state of the sensor 24.

[0053] In the example shown in FIG. 3, the dirt state of the sensor 24 is determined using an obstacle (wall) on the right side of the autonomous mobile device 1, so that the dirt state of the sensor 24 can also be determined for the scanning direction of angle D2 as shown in FIG. 7. However, the dirt state of the sensor 24 cannot be determined for the scanning direction of the range of the detectable range Ar where the laser light is not irradiated on the wall. Therefore, the control unit 21 may rotate the distance measuring device 2 so that the position of the detectable range Ar relative to the obstacle is changed. Specifically, when the dirt state determination process for the first scanning range (see FIG. 3 and FIG. 7) including the obstacle in the detectable range Ar of the sensor 24 is completed, the determination processing unit 213 rotates the sensor 24 so that the obstacle is included in the second scanning range Ar of the detectable range Ar, as shown in FIG. 8. For example, when the detectable range Ar is set to 270 degrees, the determination processing unit 213 rotates the sensor 24 three times by 90 degrees each. In this way, the determination processing unit 213 rotates the sensor 24 multiple times to determine the dirt state in each scanning direction. This makes it possible to determine the state of contamination of the sensor 24 for the entire detectable range Ar.

[0054] In another embodiment, the autonomous mobile device 1 may be rotated (turned) without rotating the distance measuring device 2 (sensor 24) to determine the dirt state of the sensor 24 for the entire detectable range Ar.

[0055] [Dirt detection process] Hereinafter, an example of the dirt detection process executed by the distance measuring device 2 will be described with reference to FIG.

[0056] The present disclosure can be understood as a disclosure of a dirt detection method that executes one or more steps included in the dirt detection process. The dirt detection method is an example of the detection method of the present disclosure. One or more steps included in the dirt detection process described here may be omitted as appropriate. The steps in the dirt detection process may be executed in a different order as long as the same action and effect is produced. Furthermore, although an example is described here in which the control unit 21 of the distance measuring device 2 executes each step in the dirt detection process, the control unit 11 of the autonomous driving device 1 and the control unit 21 of the distance measuring device 2 may cooperate to execute each step in the dirt detection process. Another embodiment of the dirt detection method may also be one in which one or more processors execute each step in the dirt detection process in a distributed manner.

[0057] In step S1, the control unit 21 of the distance measuring device 2 acquires distance measurement data. Specifically, the control unit 21 acquires distance measurement data measured by the sensor 24 for each scanning direction of the detectable range Ar. For example, the control unit 21 acquires multiple distance measurement data for each of all scanning directions of the detectable range Ar by rotating the sensor 24, for example.

[0058] Next, in step S2, the control unit 21 calculates the variance of the multiple distance measurement data for each scanning direction. For example, the control unit 21 calculates the variance based on the multiple distance measurement data in the scanning direction at angle D1 shown in Fig. 3, and calculates the variance based on the multiple distance measurement data in the scanning direction at angle D2 shown in Fig. 7. The control unit 21 calculates the variance for each of all scanning directions in the detectable range Ar.

[0059] Next, in step S3, the control unit 21 determines the dirt state (degree of dirt) in each scanning direction based on the variance calculated for each scanning direction. The control unit 21 determines the degree of dirt of the sensor 24 for each scanning direction of the detectable range Ar.

[0060] Next, in step S4, the control unit 21 determines whether the determined degree of contamination is equal to or greater than a first threshold value. The threshold value is a threshold value for the size of the range in which contamination is detected (the number of scanning directions), and for example, the first threshold value is set to a scanning direction of "five places." If the control unit 21 determines that the determined degree of contamination is equal to or greater than the first threshold value (S4: Yes), it shifts the process to step S5, and if the control unit 21 determines that the determined degree of contamination is less than the first threshold value (S4: No), it shifts the process to step S61.

[0061] In step S61, the control unit 21 causes the autonomous driving device 1 to normally drive. Specifically, the control unit 21 filters the distance measurement data in the scanning direction and ignores object detection in the scanning direction. The control unit 21 also outputs an instruction to the control unit 11 to continue driving the autonomous driving device 1, thereby causing the autonomous driving device 1 to continue the autonomous driving.

[0062] If the determined degree of contamination is equal to or greater than the first threshold (S4: Yes), in step S5, the control unit 21 further determines whether the determined degree of contamination is equal to or greater than a second threshold. For example, the second threshold is set in the scanning direction of "10 locations." If the control unit 21 determines that the determined degree of contamination is equal to or greater than the second threshold (S5: Yes), it shifts the process to step S63, and if the control unit 21 determines that the determined degree of contamination is less than the second threshold (S5: No), it shifts the process to step S62.

[0063] In step S62, the control unit 21 causes the autonomous mobile device 1 to travel with restrictions. Specifically, the control unit 21 cancels the distance measurement data in the scanning direction, and outputs an instruction to the control unit 11 to decelerate the autonomous mobile device 1, thereby slowing down the traveling speed. The control unit 21 may also notify the user of messages such as that the sensor 24 is dirty, that the sensor 24 needs to be cleaned, the predicted time when cleaning will be required, and that the autonomous mobile device 1 should be decelerated.

[0064] In step S63, the control unit 21 stops the autonomous driving device 1. Specifically, the control unit 21 outputs an instruction to the control unit 11 to stop the autonomous driving device 1, thereby halting the autonomous driving. The control unit 21 may also notify the user of a message urging the user to immediately clean the sensor 24.

[0065] After executing the countermeasure processing shown in steps S61, S62, and S63, the control unit 21 ends the dirt detection processing. Note that the control unit 21 executes the dirt detection processing while the autonomous mobile device 1 is stopped at a predetermined position. In another embodiment, the control unit 21 may execute the dirt detection processing while the autonomous mobile device 1 is autonomously traveling.

[0066] Furthermore, the control unit 21 may execute the dirt detection process at a predetermined cycle. For example, when a maintenance schedule for the sensor 24 is set in advance, the control unit 21 executes the dirt detection process periodically in accordance with the maintenance schedule. Furthermore, the control unit 21 may set a schedule for the dirt detection process from the next time onward, based on the degree of dirt determined by the dirt detection process.

[0067] As described above, the distance measuring device 2 according to this embodiment acquires a plurality of distance measurement data measured by a distance measuring unit (sensor 24) that measures the distance to an object in a predetermined distance measuring direction (scanning direction), and calculates the variation (e.g., variance) of the acquired plurality of distance measurement data. In addition, the distance measuring device 2 judges the dirt state of the distance measuring unit based on the calculated variation.

[0068] According to the above configuration, even if the optical window of the sensor 24 is contaminated with fine dust or the like, it is possible to determine the degree of contamination of the sensor 24 from the value of the variation in the distance measurement data.

[0069] Specifically, when the sensor 24 is not dirty and distances can be measured correctly, the distance data remains almost unchanged (see FIG. 4A), and the variance of distance data for a certain period of time in each scanning direction is very small. However, when the sensor 24 is dirty with minute dust particles or the like, the actual distance to an obstacle varies (see FIG. 4B). For this reason, as shown in FIG. 5B, the variance of the distance data becomes larger compared to when distances are measured correctly (see FIG. 5A). In this case, the greater the degree of dirt, the fewer the number of actual obstacles that can be detected, and the more distance data is detected between the obstacles. This makes it possible to determine the degree of dirt based on the magnitude of the variance.

[0070] As described above, by determining the degree of dirt using the variation (variance), it is possible to suppress the effects of temporary noise and suddenly occurring obstacles (such as a person or object passing by) and accurately determine the dirt on the sensor 24. Note that if the sensor 24 is noticeably dirty and the laser light directed toward the obstacle is completely blocked, extremely short distance measurement data will be detected, reducing the variation (see Figs. 6A and 6B), but the dirt on the sensor 24 can be determined based on the average value of the detection distances in the distance measurement data, etc.

[0071] [Other embodiments] The distance measuring device 2 according to the present disclosure is not limited to the above-described embodiment. In another embodiment, the distance measuring device 2 may determine the dirt state of the sensor 24 based on an estimated distance from the sensor 24 to an object in map data of a driving area in which the autonomous driving device 1 can travel and multiple distance measurement data.

[0072] Specifically, the autonomous mobile device 1 creates a map M1 (see FIG. 10) in advance and is able to estimate its current position within the map M1 from the distance measurement data of the sensor 24. When the autonomous mobile device 1 is able to estimate its own position, it can estimate the distance between its own position within the map M1 and an object (obstacle) within the map M1. Therefore, the distance measurement device 2 can determine the dirt state of the sensor 24 at any position during traveling by using the distance estimated from the map data and the distance of the actual distance measurement data of the sensor 24. Furthermore, the distance measurement device 2 can determine the dirt state of the sensor 24 while the autonomous mobile device 1 is traveling.

[0073] Here, the distance to the obstacle in each scanning direction changes with the traveling position as the autonomous mobile device 1 continues traveling, but the dirt state of the sensor 24 can be determined by calculating the ratio between the distance estimated based on the position information of the autonomous mobile device 1's self-position (current position) and map information and the distance measurement data actually acquired from the sensor 24, and calculating the variance of the calculated ratio. Fig. 11A and Fig. 12A show an example of a case where no dirt is attached to a position of the sensor 24 in a predetermined scanning direction, and Fig. 11B and Fig. 12B show an example of a case where minute dirt is attached to a position of the sensor 24 in a predetermined scanning direction.

[0074] When the autonomous driving device 1 is driving, a new obstacle may be placed, and distance measurement data may be acquired that does not match the distance estimated from the map created in advance (see Figures 11A and 11B). However, the autonomous driving device 1 will not always capture the obstacle in the same scanning direction while it is driving, so it is possible to stably detect noise by calculating the variance from the distance measurement data over a certain period of time.

[0075] According to the above configuration, by storing distance measurement data for a certain period of time, the influence of sudden noise can be suppressed, and since the surrounding environment changes while the autonomous mobile device 1 is traveling, the influence of places with a lot of noise due to environmental factors (places with a lot of dust) can be minimized. In addition, the autonomous mobile device 1 can determine the dirt state of the sensor 24 without stopping while traveling, which can prevent a decrease in work efficiency.

[0076] In another embodiment of the present disclosure, the distance measuring device 2 does not have to be mounted on the autonomous driving device 1. For example, the distance measuring device 2 can be installed in a predetermined location, and the control unit 21 can execute the dirt detection process (see FIG. 9) to determine the dirt state of the sensor 24. Furthermore, each processing unit included in the distance measuring device 2 may be provided in an external device different from the distance measuring device 2. For example, a server capable of data communication with the distance measuring device 2 may include each processing unit (acquisition processing unit 211, calculation processing unit 212, determination processing unit 213, response processing unit 214), and the server may execute the dirt detection process (see FIG. 9).

[0077] Furthermore, when the distance measuring device 2 is mounted on the autonomous mobile device 1, the control unit 11 of the autonomous mobile device 1 may include the processing units (the acquisition processing unit 211, the calculation processing unit 212, the determination processing unit 213, and the handling processing unit 214). In this case, the control unit 11 of the autonomous mobile device 1 may execute the dirt detection process (see FIG. 9).

[0078] That is, the detection system according to the present disclosure may be configured by the ranging device 2 alone, may be configured by the autonomous mobile device 1 alone, or may be configured by the server alone. Moreover, the detection system may be configured by one or more components among the ranging device 2, the autonomous mobile device 1, and the server. Moreover, one or more components among the ranging device 2, the autonomous mobile device 1, and the server may execute the detection method according to the present disclosure. Moreover, the detection program according to the present disclosure may be stored in one or more components among the ranging device 2, the autonomous mobile device 1, and the server.

[0079] [Disclosure Note] The following will provide an outline of the disclosure extracted from the above-described embodiment. Note that the configurations and processing functions described in the following supplementary notes can be selected and combined in any desired manner.

[0080] <Appendix 1> an acquisition processing unit that acquires a plurality of distance measurement data measured by a distance measuring unit that measures a distance to an object in a predetermined distance measurement direction; a calculation processing unit that calculates a variance of the plurality of distance measurement data acquired by the acquisition processing unit; a determination processing unit that determines a dirt state of the distance measuring unit based on the variation calculated by the calculation processing unit; A detection system comprising:

[0081] <Appendix 2> The variation is calculated by dividing the sum of squares of differences between the plurality of distance measurement data and an average value of the plurality of distance measurement data by the number of data of the plurality of distance measurement data. 2. The detection system of claim 1. <Appendix 3> The determination processing unit determines that the distance measuring unit is dirty when the variation calculated by the calculation processing unit is equal to or greater than a threshold value. 2. The detection system of claim 1.

[0082] <Appendix 4> When the distance measuring unit is installed on a moving body, the calculation processing unit calculates the variation and the determination processing unit determines a dirt state of the distance measuring unit on the condition that the moving body is stopped. A detection system according to any one of appendix 1 to 3.

[0083] <Appendix 5> the acquisition processing unit acquires the plurality of distance measurement data for each of a plurality of distance measurement directions; the calculation processing unit calculates the variation for each of the plurality of distance measurement directions; the determination processing unit determines the dirt state for each of the plurality of distance measurement directions. A detection system according to any one of claims 1 to 4.

[0084] <Appendix 6> a response processing unit that executes a process according to a result of the determination by the determination processing unit when the distance measuring unit is installed in a moving body, The response processing unit is When a range of the distance measurement direction in which the dirt is detected is less than a first range among the plurality of distance measurement directions, the determination process of the determination processing unit in the range is stopped and the moving body is allowed to continue traveling; When the range of the distance measurement direction in which the dirt is detected is equal to or larger than the first range and smaller than the second range among the plurality of distance measurement directions, the determination process of the determination processing unit in the range is stopped and the moving body is caused to travel at a reduced speed; when the range of the distance measurement direction in which the dirt is detected is equal to or larger than the second range among the plurality of distance measurement directions, stopping the traveling of the moving object and notifying a user. 6. The detection system of claim 5.

[0085] <Appendix 7> when the determination process of the dirt state is completed for a first scanning range in the scanning range of the distance measuring unit, the determination process rotates the distance measuring unit so that the object is included in a second scanning range in the scanning range. A detection system according to any one of claims 1 to 6.

[0086] <Appendix 8> the determination processing unit determines the contamination state in each scanning direction by rotating the distance measuring unit a plurality of times. 8. The detection system of claim 7.

[0087] <Appendix 9> When the distance measuring unit is installed in a moving body, the determination processing unit determines the dirt state based on an estimated distance from the distance measuring unit to the object in map data of a traveling area in which the moving body can travel and the plurality of distance measurement data. A detection system according to any one of appendix 1 to 8.

[0088] <Appendix 10> The determination processing unit determines the dirt state while the moving object is traveling. 10. The detection system of claim 9. [Explanation of symbols]

[0089] 1: Autonomous driving device 2: Distance measuring device 11: Control section 12: Storage section 13: Communications Department 21: Control section 22: Storage section 23: Communications Department 24: Sensor 111: Driving processing unit 211: Acquisition processing unit 212: Calculation processing unit 213: Judgment processing unit 214: Handling processing unit Ar: Detection range M1: Map

Claims

1. an acquisition processing unit that acquires a plurality of distance measurement data measured by a distance measuring unit that measures a distance to an object in a predetermined distance measurement direction; a calculation processing unit that calculates a variance of the plurality of distance measurement data acquired by the acquisition processing unit; a determination processing unit that determines a dirt state of the distance measuring unit based on the variation calculated by the calculation processing unit; A detection system comprising:

2. The variation is calculated by dividing the sum of squares of differences between the plurality of distance measurement data and an average value of the plurality of distance measurement data by the number of data of the plurality of distance measurement data. The detection system of claim 1 .

3. The determination processing unit determines that the distance measuring unit is dirty when the variation calculated by the calculation processing unit is equal to or greater than a threshold value. The detection system of claim 1 .

4. When the distance measuring unit is installed on a moving body, the calculation processing unit calculates the variation and the determination processing unit determines a dirt state of the distance measuring unit on the condition that the moving body is stopped. The detection system of claim 1 .

5. the acquisition processing unit acquires the plurality of distance measurement data for each of a plurality of distance measurement directions; the calculation processing unit calculates the variation for each of the plurality of distance measurement directions; the determination processing unit determines the dirt state for each of the plurality of distance measurement directions. The detection system of claim 1 .

6. a response processing unit that executes a process according to a result of the determination by the determination processing unit when the distance measuring unit is installed in a moving body, The response processing unit is When a range of the distance measurement direction in which the dirt is detected is less than a first range among the plurality of distance measurement directions, the determination process of the determination processing unit in the range is stopped and the moving body is allowed to continue traveling; When a range of the distance measurement direction in which the dirt is detected is equal to or larger than the first range and smaller than a second range among the plurality of distance measurement directions, the determination process of the determination processing unit in the range is stopped and the moving body is caused to decelerate; when a range of the distance measurement direction in which the dirt is detected is equal to or larger than the second range among the plurality of distance measurement directions, stopping the traveling of the moving object and notifying a user of the stopping of the traveling of the moving object; The detection system of claim 5 .

7. When the distance measuring unit is installed in a moving body, the determination processing unit determines the dirt state based on an estimated distance from the distance measuring unit to the object in map data of a traveling area in which the moving body can travel and the plurality of distance measurement data. The detection system of claim 1 .

8. The determination processing unit determines the dirt state while the moving object is traveling. The detection system of claim 7.

9. acquiring a plurality of distance measurement data measured by a distance measuring unit that measures a distance to an object in a predetermined distance measurement direction; Calculating a variance in the plurality of distance measurement data; determining a state of contamination of the distance measuring unit based on the variation; and The detection method is executed by one or more processors.

10. acquiring a plurality of distance measurement data measured by a distance measuring unit that measures a distance to an object in a predetermined distance measurement direction; Calculating a variance in the plurality of distance measurement data; determining a state of contamination of the distance measuring unit based on the variation; A detection program for causing one or more processors to execute the above.

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