System and program

Abstract

According to the invention, the obstacle area detection capability is improved. A system of the present disclosure is used for detecting a travel obstacle region that is a region of an obstacle for travel of a work machine, and includes a region angle detection unit, a region height detection unit, an angle obstacle region detection unit, a height obstacle region detection unit, and a travel obstacle region detection unit. The area angle detection unit detects an angle of the traveling surface based on a distance image around the work machine. The area height detection unit detects a height of the traveling surface based on the distance image. The angular obstacle region detection unit detects an angular obstacle region as an obstacle region based on the detected angle. The height obstacle region detection unit detects a height obstacle region as an obstacle region based on the detected height. The travel obstacle region detection unit detects a travel obstacle region based on the detected angle obstacle region and the detected height obstacle region.

Description

Technical Field

[0001] The present disclosure relates to a system and a program. Background Art

[0002] A system is proposed to prevent accidents such as rollovers when a working device such as an excavator is traveling at a construction site, etc. For example, a crane is proposed that generates a three-dimensional map by imaging the work site, recognizes the inclination angle of the road surface based on the three-dimensional map, and compares the inclination angle with the gradeability of the working device to determine whether it is possible to travel (for example, see Patent Document 1).

[0003] Reference List

[0004] Patent Literature

[0005] Patent document 1: JP 2018-095365 A. Summary of the invention

[0006] Technical issues

[0007] However, in the above-mentioned prior art, since the dangerous area is determined based on whether the excavator of the system is in contact with the road surface, there is a problem that the obstacle area cannot be sufficiently detected.

[0008] Therefore, the present disclosure proposes a system and program for improving the ability to detect obstacle areas.

[0009] Solution to the problem

[0010] The system according to the present disclosure is a system for detecting a traveling obstacle area as an area that hinders the travel of a working machine, and includes: an area angle detection unit, an area height detection unit, an angle obstacle area detection unit, a height obstacle area detection unit, and a traveling obstacle area detection unit. The area angle detection unit detects the angle of the traveling surface based on a distance image around the working machine. The area height detection unit detects the height of the traveling surface based on the distance image. The angle obstacle area detection unit detects an angle obstacle area as an obstacle area based on the detected angle. The height obstacle area detection unit detects a height obstacle area as an obstacle area based on the detected height. The traveling obstacle area detection unit detects the traveling obstacle area based on the detected angle obstacle area and the detected height obstacle area.

[0011] The program according to the present disclosure is a program for detecting a traveling obstacle area as an area that hinders the travel of a working machine, and includes: an area angle detection process, which detects the angle of a traveling surface based on a distance image around the working machine; an area height detection process, which detects the height of the traveling surface based on the distance image; an angle obstacle area detection process, which detects an angle obstacle area as an obstacle area based on the detected angle; a height obstacle area detection process, which detects a height obstacle area as an obstacle area based on the detected height; and a traveling obstacle area detection process, which detects a traveling obstacle area based on the detected angle obstacle area and the detected height obstacle area. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1A : is a diagram showing an example of a travel path of a working device according to an embodiment of the present disclosure.

[0013] Figure 1B is a diagram illustrating an example of a travel path according to an embodiment of the present disclosure.

[0014] Figure 2 : is a diagram showing a configuration example of a traveling obstacle area detection system according to an embodiment of the present disclosure.

[0015] Figure 3 is a diagram illustrating an example of a data conversion process according to an embodiment of the present disclosure.

[0016] Figure 4 is a diagram illustrating an example of an angle detection process according to an embodiment of the present disclosure.

[0017] Figure 5 is a diagram illustrating an example of a neighboring vector generation process according to an embodiment of the present disclosure.

[0018] Figure 6 is a diagram illustrating an example of a normal vector generation process according to an embodiment of the present disclosure.

[0019] Figure 7 is a diagram illustrating an example of an angle calculation process according to an embodiment of the present disclosure.

[0020] Figure 8 is a diagram illustrating an example of a detection angle according to an embodiment of the present disclosure.

[0021] Fig. 9 is a diagram illustrating an example of an angular obstacle area detection process according to an embodiment of the present disclosure.

[0022] Fig.10 is a diagram illustrating an example of an angular obstacle area according to an embodiment of the present disclosure.

[0023] Fig.11 is a diagram illustrating an example of a high obstacle area detection process according to an embodiment of the present disclosure.

[0024] Fig.12 is a diagram illustrating an example of a high obstacle area according to an embodiment of the present disclosure.

[0025] Fig.13 : is a diagram showing a configuration example of a traveling obstacle region detection unit according to an embodiment of the present disclosure.

[0026] Fig.14 is a diagram illustrating an example of a traveling obstacle region detection process according to an embodiment of the present disclosure.

[0027] Fig.15 is a diagram illustrating an example of an occlusion area according to an embodiment of the present disclosure.

[0028] Fig.16 is a diagram illustrating an example of a traveling obstacle region detection process according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following describes the embodiments of the present disclosure in detail with reference to the accompanying drawings. Note that in the embodiments described below, the same reference numerals and signs are given to the same parts to omit repeated descriptions.

[0030] [Travel path of the working device]

[0031] Figure 1A and Figure 1B is a diagram illustrating an example of a travel path of a working device according to an embodiment of the present disclosure. Figure 1A 1 is a diagram showing an example of a travel path of a working machine 10. The working machine 10 shown in the figure is assumed to be an excavator traveling with a crawler 11. The working machine 10 performs operations of carrying objects such as soil and sand at a construction site, etc. At a construction site, etc., there is sometimes a travel obstacle area that is a travel obstacle such as a steep slope or a ditch (groove) formed on the ground. As a travel obstacle area, an object 21, slopes (hills) 22 and 23, and a ditch 26 are shown in the figure. Object 21 is a cube with a side surface with an angle of 90°. Slope 22 is assumed to be a slope with an angle of 25°, and slope 23 is assumed to be a slope with an angle of 50°. Ditch 26 is assumed to be a ditch with a width of 2m and a depth of 1m. The ditch 26 includes a bottom surface 25 and a slope 24 with an angle of 90°. Note that the ground 20 shown in the figure is equivalent to a surface with an angle of 0°.

[0032] By detecting such a traveling obstacle area and presenting it to the user, the occurrence of accidents such as collisions and rollovers can be reduced. The traveling obstacle area detection system of the present disclosure detects such a traveling obstacle area. The sensor 12 is arranged in the working machine 10 shown in the figure. The image around the working machine 10 is acquired by the sensor 12 and is input to the traveling obstacle area detection system. For example, a distance measuring sensor that generates a distance image can be used as the sensor 12. Here, the distance image is also called a depth map and is an image that reflects distance information for each pixel.

[0033] Figure 1B It is schematically shown Figure 1A 301 is an image of an image of a travel path shown in FIG. In the image 301 shown in the figure, a flat ground 20, an object 21, slopes 22 and 23, and a slope 24 and a bottom surface 25 of a ditch 26 are arranged. The operation of the travel obstacle area detection system of the present disclosure is explained with reference to the image 301 as an example of a travel path.

[0034] [Traveling obstacle area detection system]

[0035] Figure 2 1 is a diagram showing a configuration example of a traveling obstacle region detection system according to an embodiment of the present disclosure. The figure is a block diagram showing a configuration example of a traveling obstacle region detection system 100. The traveling obstacle region detection system 100 includes a data conversion unit 110, a region angle detection unit 120, an angle obstacle region detection unit 140, a region height detection unit 150, a height obstacle region detection unit 170, threshold generation units 130 and 160, and a traveling obstacle region detection unit 180.

[0036] The data conversion unit 110 converts the range image as input data into point cloud data, and outputs the point cloud data to the area angle detection unit 120 and the area height detection unit 150 .

[0037] The area angle detection unit 120 detects the angle of the travel surface based on the point cloud data. The detected angle is output to the angle obstacle area detection unit 140.

[0038] The angular obstacle region detecting unit 140 detects an angular obstacle region as an obstacle region based on the angle of the traveling surface, and the detected angular obstacle region is output to the traveling obstacle region detecting unit 180 .

[0039] The threshold value generating unit 130 generates a threshold value for detecting an angular obstacle area in the angular obstacle area detecting unit 140 .

[0040] The area height detection unit 150 detects the height of the travel surface based on the point cloud data, and the detected height is output to the height obstacle area detection unit 170 .

[0041] The high obstacle region detection unit 170 detects a high obstacle region as an obstacle region based on the height of the traveling surface. The detected high obstacle region is output to the traveling obstacle region detection unit 180.

[0042] The threshold value generating unit 160 generates a threshold value for detecting a high obstacle region in the high obstacle region detecting unit 170 .

[0043] The traveling obstacle region detecting unit 180 detects the traveling obstacle region based on the angle obstacle region and the height obstacle region. The detected traveling obstacle region is output to an external device, for example, a display device.

[0044] In the following description, details of the processing of each unit are described.

[0045] [Data conversion processing]

[0046] Figure 3 is a diagram showing an example of data conversion processing according to an embodiment of the present disclosure. The figure is a flowchart showing an example of data conversion processing (S110) in the data conversion unit 110. First, the input distance image is normalized (step S111). The normalization converts the distance image into point cloud data. The point cloud data consists of an array of x, y and z axis coordinates of each point. Known methods can be applied to convert the distance image into point cloud data.

[0047] Next, downsampling is performed on the point cloud data (step S112). This downsampling is a process for thinning row and column data for each of the arrays of coordinates of the x, y, and z axes. Through this downsampling, the amount of processing can be reduced and noise can be reduced.

[0048] Next, coordinate correction is performed on the down-sampled point cloud data (step S113 ). The coordinate correction is a process for converting the point cloud data into global coordinates. Figure 1A The sensor 12 is shown mounted to the body of the work machine 10. The point cloud data is corrected according to the mounting height and mounting angle of the sensor 12.

[0049] [Angle detection processing]

[0050] Figure 4is a diagram showing an example of an angle detection process according to an embodiment of the present disclosure. The figure is a flowchart showing an example of an angle detection process (S120) in the regional angle detection unit 120. First, the regional angle detection unit 120 selects a point of interest of the point cloud data (step S121). Next, the regional angle detection unit 120 generates an adjacent vector for the selected point of interest (step S122). This is a process for generating a vector pointing to two points adjacent in the row direction and column direction of the point of interest. Next, the regional angle detection unit 120 generates a normal vector (step S123). This can be generated by calculating the outer product of two adjacent vectors. Next, the regional angle detection unit 120 determines whether the normal vector has been calculated for all points (step S124). The regional angle detection unit 120 transfers to step S125 when normal vectors are generated for all points (step S124, yes), and selects other points of interest (step S121) when there are points for which normal vectors are not generated (step S124, no).

[0051] In step S125, the regional angle detection unit 120 selects a point of interest of the point cloud data (step S125). Next, the regional angle detection unit 120 calculates the angle relative to the selected point of interest (step S126). This can be performed by calculating the inner product of the normal and the unit vector in the y-axis direction. Next, the regional angle detection unit 120 adjusts the angle sign (step S127). This is to reverse the sign of the angle when the slope angle is a depression angle. This can be performed based on the value of the z-axis array of the point. Specifically, when the value of the z-axis array is a positive value, the sign of the angle is reversed. Next, the regional angle detection unit 120 determines whether the angle has been calculated for all points (step S128). When the angle has been calculated for all points, the regional angle detection unit 120 ends the processing (step S128, yes), and when there is a point for which the angle has not been calculated (step S125), the regional angle detection unit 120 selects another point of interest (step S128, no).

[0052] [Adjacent vector generation process]

[0053] Figure 5 is a diagram showing an example of adjacent vector generation processing according to an embodiment of the present disclosure. In the figure, "x", "y", and "z" represent arrays of coordinates of the x, y, and z axes of the point cloud, respectively. In the array, a focus point 310 is selected, and a point 311 adjacent to the focus point 310 in the row direction and a point 312 adjacent to the column direction are selected. Then, a W vector 313 from the focus point 310 to the point 311 and an H vector 314 from the focus point 310 to the point 312 are generated.

[0054] [Normal vector generation process]

[0055] Figure 61 is a diagram showing an example of a normal vector generation process according to an embodiment of the present disclosure. As shown in the figure, the normal vector A can be calculated by calculating the outer product A of the W vector and the H vector and normalizing the outer product A with a norm of "1".

[0056] [Angle calculation processing]

[0057] Figure 7 is a diagram showing an example of angle calculation processing according to an embodiment of the present disclosure. As shown in the figure, the angle of the inclined surface relative to the horizontal direction can be calculated by calculating the inner product of the normal vector A and the unit vector "y" in the y-axis direction. The calculated angle is stored in the angle data array.

[0058] [Detection angle]

[0059] Figure 8 is a diagram showing an example of a detection angle according to an embodiment of the present disclosure. Figure 1B A diagram of angles detected by the area angle detection unit 120 is superimposed and displayed on the image 301 shown. In the image 317 shown in the figure, angles of 25° and 50° are added to the slopes 22 and 23, respectively. An angle of 90° is added to the side surface of the object 21 and the slope 24 of the ditch 26. 0° is added to the area other than the above-mentioned area.

[0060] [Angular obstacle area detection processing]

[0061] Fig. 9 1 is a diagram showing an example of an angular obstacle region detection process according to an embodiment of the present disclosure. The figure is a flowchart showing an example of an angular obstacle region detection process (S130) in the angular obstacle region detection unit 140. First, the angular obstacle region detection unit 140 calculates an absolute angle (step S131). This can be performed by correcting the angle based on the inclination of the working machine 10.

[0062] Next, the angular obstacle region detection unit 140 detects the angular obstacle region (step S132). The angular obstacle region detection unit 140 detects the angular obstacle region based on the threshold value generated by the threshold value generation unit 130. The threshold value generation unit 130 may output a predetermined value (e.g., 30°) as the threshold value. The angular obstacle region detection unit 140 may detect a region with an angle higher than the threshold value as an angular obstacle region.

[0063] The threshold value generation unit 130 can adjust the threshold value based on the information about the working machine 10. For example, the weight, size, center of gravity, angle, travel direction (handle information), speed, etc. of the working machine 10 correspond to the information about the working machine 10. In addition, the threshold value generation unit 130 can adjust the threshold value based on the information about the surrounding environment. For example, the weather, temperature, humidity, geology of the travel surface, etc. correspond to the information about the surrounding environment. Note that the information about the working machine 10 and the information about the surrounding environment can be acquired from an external device (e.g., a cloud server).

[0064] [Angle Obstacle Area]

[0065] Fig.10 is a diagram showing an example of an angular obstacle area according to an embodiment of the present disclosure. Figure 8 317 shown in FIG. 317 is overlapped and displays an angle obstacle area. The area indicated by the dotted line in the image 321 shown in the figure corresponds to the angle obstacle area. As shown in the figure, the angle obstacle area 322 is added to the slope 23 with an angle of 50° and the side surface of the object 21 with an angle of 90°, and the angle obstacle area 323 is added to the inclined surface 24 with an angle of 90°.

[0066] [High obstacle area detection and processing]

[0067] Fig.11 1 is a diagram showing an example of a high obstacle region detection process according to an embodiment of the present disclosure. The figure is a flowchart showing an example of a high obstacle region detection process (S140) in the high obstacle region detection unit 170. Note that the process shown in the figure includes the region height detection process in the region height detection unit 150.

[0068] First, the region height detection unit 150 generates a height map (step S141). The height map is an array representing the height of each region. Figure 5 An array of y-coordinates that specify the y-axis that can be applied to the heightmap.

[0069] Next, the high obstacle area detection unit 170 detects a high position obstacle area (step S142). Specifically, the high obstacle area detection unit 170 detects an area at a position higher than the high position threshold generated by the threshold generation unit 160 as a high position obstacle area. The high obstacle area detection unit 170 detects a low position obstacle area (step S143). Specifically, the high obstacle area detection unit 170 detects an area at a position lower than the low position threshold generated by the threshold generation unit 160 as a low position obstacle area. The threshold generation unit 130 may output a predetermined value (e.g., a high position threshold of 0.5 m and a low position threshold of -0.5 m) as a threshold. As described above, by detecting the high position obstacle area and the low position obstacle area respectively, different types of processing may be performed for the high position obstacle area and the low position obstacle area respectively.

[0070] Note that the threshold value generation unit 160 may adjust the threshold value according to the information about the working machine 10. For example, the size of the working machine 10 corresponds to the information about the working machine 10.

[0071] [High obstacle area]

[0072] Fig.12 is a diagram showing an example of a high obstacle area according to an embodiment of the present disclosure. Figure 8 317 shown in FIG. 31 and a diagram of a high obstacle area is superimposed and displayed. The area indicated by the dashed line in the image 331 in the figure corresponds to the high obstacle area 332. The area indicated by the double dashed line in the image 331 in the figure corresponds to the low obstacle area 333. As shown in the figure, the high obstacle area 332 is added to the high position areas of the slopes 22 and 23 and the upper surface of the object 21. The low obstacle area 333 is added to the low position areas of the slope 24 and the bottom surface 25.

[0073] [Traveling obstacle area detection unit]

[0074] Fig.13 1 is a diagram showing a configuration example of a traveling obstacle region detection unit according to an embodiment of the present disclosure. The figure is a block diagram showing a configuration example of the traveling obstacle region detection unit 180. The traveling obstacle region detection unit 180 includes an abnormal region detection unit 181, an object detection unit 182, and an occlusion region detection unit 183.

[0075] The abnormal area detection unit 181 detects abnormal areas of the angle obstacle area and the height obstacle area. The angle obstacle area and the height obstacle area other than the abnormal area detected by the abnormal area detection unit 181 are travel obstacle areas.

[0076] The object detection unit 182 detects an object placed on the traveling surface. The object detection unit 182 detects an object that obstructs the traveling, such as a person or another working machine 10 .

[0077] The obstruction area detection unit 183 detects an obstruction area. The obstruction area is an area where the angle and height on the travel surface cannot be detected. For example, an area hidden by a cliff or the like corresponds to the obstruction area. By detecting the obstruction area as an obstacle area, the safety of the working machine 10 can be improved.

[0078] [Traveling obstacle area detection processing]

[0079] Fig.14 1 is a diagram showing an example of a traveling obstacle area detection process according to an embodiment of the present disclosure. The figure is a block diagram showing an example of a process of the traveling obstacle area detection unit 180. First, the abnormal area detection unit 181 detects an abnormal area of ​​the angle obstacle area (step S151). For example, a slope area whose height is lower than a predetermined threshold corresponds to the abnormal area. This is because, even in a slope with a steep angle, an area whose height is lower than the height of the crawler 11 of the working machine 10 can be crossed. The detected abnormal area is excluded from the angle obstacle area.

[0080] Next, the abnormal area detection unit 181 detects abnormal areas of the high obstacle area (step S152). For example, an area whose width is less than a predetermined threshold corresponds to an abnormal area. This is because a ditch 26 with a relatively narrow width can be crossed. The detected abnormal area is excluded from the high obstacle area.

[0081] Next, the object detecting unit 182 detects an object (step S153). The area of ​​the detected object is added to the traveling obstacle area.

[0082] Next, the occlusion region detection unit 183 detects an occlusion region (step S154 ). The detected occlusion region is added to the traveling obstacle region.

[0083] [Occlusion Area]

[0084] Fig.15is a diagram showing an example of an occlusion area according to an embodiment of the present disclosure. The figure is a diagram showing a state in which a distance image of a travel surface is detected by a sensor 12 of a working machine 10. The dotted line in the figure represents the field of view of the sensor 12. A step 29 exists on the ground 20 shown in the figure. The area hidden by the cliff 31 in the upper part of the step 29 observed from the sensor 12 is the occlusion area 30. The dotted line in the figure is a line connecting the sensor 12 and the cliff 31. Assuming that the dotted line is at a position 32 intersecting the ground 20 below the cliff 31, a portion where the distance from the cliff 31 to the position 32 is discontinuous is generated in the distance image. The occlusion area detection unit 183 can detect the area where the distance is discontinuous as the occlusion area 30.

[0085] [Traveling obstacle detection processing]

[0086] Fig.16 is a diagram showing an example of a traveling obstacle area detection process according to an embodiment of the present disclosure. The figure is a flowchart showing an example of a process of a traveling obstacle area detection system 100. First, the data conversion unit 110 performs a data conversion process (step S110). Next, the regional angle detection unit 120 performs a regional angle detection process (step S120). Next, the angle obstacle area detection unit 140 performs an angle obstacle area detection process (step S130). Next, the regional height detection unit 150 and the height obstacle area detection unit 170 perform a height obstacle area detection process (step S140). Next, the traveling obstacle area detection unit 180 performs a traveling obstacle area detection process (step S150). Through the above-mentioned process, a traveling obstacle area can be detected. The detected traveling obstacle area is output to an external device.

[0087] As described above, the traveling obstacle region detection system 100 of the present disclosure detects the traveling obstacle region based on the angle and height of the traveling surface, thereby improving the detection capability of the obstacle region.

[0088] Note that the obstacle area detection system 100 disclosed herein can be applied not only to work equipment at construction sites, but also to, for example, ordinary vehicles, construction machinery, agricultural machinery, forestry machinery, disaster relief machinery, household machinery for care and cleaning, seabed probes, and planetary probes, etc.

[0089] (Other changes)

[0090] The traveling obstacle region detection system 100 in this embodiment may be implemented by a dedicated computer system, or may be implemented by a general-purpose computer system.

[0091] For example, a program for executing the above operation is stored in a computer-readable recording medium such as an optical disk, a semiconductor memory, a magnetic tape, or a flexible disk and is distributed. Then, for example, the control device is configured by installing the program in a computer and executing the above-described processing.

[0092] The communication program described above may be stored in a disk device included in a server device on a network such as the Internet so that the communication program can be downloaded to a computer. The functions described above may be implemented by the cooperation of an operating system (OS) and application software. In this case, the portion other than the OS may be stored in a medium and distributed, or the portion other than the OS may be stored in a server device so that the portion may be downloaded to a computer.

[0093] In the various processes described in the embodiments, all or part of the processes explained as being automatically performed may be performed manually, or all or part of the processes explained as being manually performed may be performed automatically by a known method. In addition, unless otherwise specifically noted, the processes, specific names, and information including various data and parameters explained in the document and shown in the figures may be optionally changed. For example, the various information shown in the figures is not limited to the information shown.

[0094] The components of the device shown are conceptual in function and do not always need to be physically configured as shown. That is, the specific form of dispersion and integration of the device is not limited to the form shown, and all or part of it can be functionally or physically dispersed and integrated into arbitrary units according to various loads, usage conditions, etc. Note that such configuration by dispersion and integration can be performed dynamically.

[0095] The above-described embodiments can be combined as appropriate within the range in which the processing contents do not contradict each other. The order of the steps of the flowcharts shown in the above-described embodiments can be changed as appropriate.

[0096] For example, the present embodiment can be implemented as any component constituting a device or a system, such as a processor used as a system large-scale integration (LSI) or the like, a module using a plurality of processors or the like, a unit using a plurality of modules or the like, or a collection obtained by further adding other functions to the unit (i.e., a component as a part of the device).

[0097] Note that in this embodiment, a system refers to a collection of multiple components (devices, modules (components, etc.). It is not important whether all components exist in the same housing. Therefore, multiple devices housed in separate housings and connected via a network and a device in which multiple modules are housed in one housing are both systems.

[0098] For example, the present embodiment may adopt a configuration of cloud computing in which one function is collaboratively shared and processed by a plurality of devices via a network.

[0099] Although the embodiments of the present disclosure are described above, the technical scope of the present disclosure is not limited to the embodiments themselves. Various changes can be made without departing from the gist of the present disclosure. Components in different embodiments and modifications can be appropriately combined.

[0100] The processing procedures described in the embodiments may be regarded as methods including these series of processes, and may be regarded as programs for causing a computer to execute these series of processes or recording media storing the programs. As the recording medium, for example, a floppy disk, a CD-ROM (Compact Disc Read Only Memory), an MO (Magneto-Optical) disk, a DVD (Digital Versatile Disk), a Blu-ray (registered trademark) disk, a magnetic disk, a semiconductor memory, a memory card, etc. may be used.

[0101] Note that the effects described in this specification are only illustrative and not limiting. Other effects may exist.

[0102] Note that the present technology can also adopt the following configurations.

[0103] (1) A system for detecting a travel obstacle area, the travel obstacle area being an area that hinders the travel of a working machine, the system comprising:

[0104] an area angle detection unit that detects an angle of a travel surface based on a range image around the work machine;

[0105] an area height detection unit for detecting the height of a traveling surface based on the range image;

[0106] an angular obstacle region detecting unit, detecting an angular obstacle region as an obstacle region based on the detected angle;

[0107] a height obstacle region detecting unit that detects a height obstacle region as an obstacle region based on the detected height; and

[0108] The traveling obstacle region detecting unit detects the traveling obstacle region based on the detected angular obstacle region and the detected height obstacle region.

[0109] (2) The system according to (1) above, wherein the angular obstacle area detection unit detects the angular obstacle area based on a threshold value of an angle.

[0110] (3) The system according to (2) above, wherein the angular obstacle area detection unit detects the angular obstacle area based on a threshold value adjusted according to information on the working machine.

[0111] (4) The system according to (2) above, wherein the angular obstacle area detection unit detects the angular obstacle area based on a threshold value adjusted according to information on the surrounding environment.

[0112] (5) The system according to any one of (1) to (4) above, wherein the high obstacle region detection unit detects the high obstacle region based on a threshold value of the height.

[0113] (6) The system according to any one of (1) to (5) above, wherein the travel obstacle region detection unit detects the travel obstacle region by removing a region having a predetermined height from among the detected angular obstacle regions.

[0114] (7) The system according to any one of (1) to (6) above, wherein the travel obstacle region detection unit detects the travel obstacle region by removing a region having a predetermined width from among the detected height obstacle regions.

[0115] (8) The system according to any one of (1) to (7) above, further comprising:

[0116] an object detection unit to detect an object placed on the traveling surface;

[0117] The traveling obstacle region detection unit further detects the region of the detected object as a traveling obstacle region.

[0118] (9) The system according to any one of (1) to (8) above, further comprising:

[0119] The occlusion region detection unit detects the occlusion region, which is a region where the angle and height on the traveling surface cannot be detected, wherein:

[0120] The traveling obstacle region detection unit further detects the detected blocked region as a traveling obstacle region.

[0121] (10) A program for detecting a travel obstacle area, the travel obstacle area being an area that hinders the travel of a working machine, the program comprising:

[0122] The area angle detection process detects the angle of the travel surface based on the range image around the work machine;

[0123] The area height detection process detects the height of the traveling surface based on the range image;

[0124] An angular obstacle region detection process, detecting an angular obstacle region as an obstacle region based on the detected angle;

[0125] a height obstacle area detection process of detecting a height obstacle area as an obstacle area based on the detected height; and

[0126] The traveling obstacle area detection process detects the traveling obstacle area based on the detected angle obstacle area and the detected height obstacle area.

[0127] Reference Symbols List

[0128] 10 Operating machinery

[0129] 12 Sensors

[0130] 100 Moving Obstacle Area Detection System

[0131] 110 Data conversion unit

[0132] 120 Area Angle Detection Unit

[0133] 130 Threshold Generation Unit

[0134] 140 degree obstacle area detection unit

[0135] 150 Area Height Detection Unit

[0136] 160 Threshold Generation Unit

[0137] 170 Height obstacle area detection unit

[0138] 180 Moving obstacle area detection unit

[0139] 181 Abnormal Area Detection Unit

[0140] 182 Object Detection Unit

[0141] 183 Occlusion region detection unit.

Claims

1. A system for detecting a travel obstacle area, wherein the travel obstacle area is an area that hinders the travel of a working machine, the system comprising: an area angle detection unit that detects an angle of a travel surface based on a range image around the work machine; an area height detection unit that detects the height of the travel surface based on the range image; an angular obstacle region detecting unit, detecting an angular obstacle region as an obstacle region based on the detected angle; a height obstacle region detecting unit that detects a height obstacle region as an obstacle region based on the detected height; as well as The traveling obstacle region detecting unit detects the traveling obstacle region based on the detected angular obstacle region and the detected height obstacle region.

2. The system according to claim 1, wherein: The angular obstacle area detection unit detects the angular obstacle area based on a threshold value of the angle.

3. The system according to claim 2, wherein: The angular obstacle area detection unit detects the angular obstacle area based on the threshold value adjusted according to information on the working machine.

4. The system according to claim 2, wherein: The angular obstacle area detection unit detects the angular obstacle area based on the threshold value adjusted according to information about the surrounding environment.

5. The system according to claim 1, wherein: The high obstacle area detection unit detects the high obstacle area based on a threshold value of the height.

6. The system according to claim 1, wherein: The traveling obstacle region detecting unit detects the traveling obstacle region by removing a region having a predetermined height from among the detected angular obstacle regions.

7. The system according to claim 1, wherein: The traveling obstacle region detecting unit detects the traveling obstacle region by removing a region having a predetermined width from among the detected height obstacle regions.

8. The system of claim 1, further comprising: an object detection unit to detect an object placed on the traveling surface; The traveling obstacle region detection unit further detects a region of the detected object as the traveling obstacle region.

9. The system of claim 1, further comprising: An occlusion region detection unit detects an occlusion region, wherein the occlusion region is a region where the angle and the height on the traveling surface cannot be detected, wherein: The traveling obstacle region detection unit further detects the detected blocking region as the traveling obstacle region.

10. A program for detecting a travel obstacle area, wherein the travel obstacle area is an area that hinders the travel of a working machine, the program comprising: an area angle detection process for detecting an angle of a travel surface based on a range image around the work machine; an area height detection process for detecting the height of the travel surface based on the range image; An angular obstacle region detection process, detecting an angular obstacle region as an obstacle region based on the detected angle; A height obstacle area detection process, detecting a height obstacle area as an obstacle area based on the detected height; as well as The traveling obstacle area detection process detects the traveling obstacle area based on the detected angle obstacle area and the detected height obstacle area.

Images