Coordinate mapping method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of intelligent robots, and provides a coordinate mapping method and device, electronic equipment and a medium. Comprising the following steps: carrying out ground area segmentation according to a camera installation pose; semantic target recognition is carried out on the segmented ground area image; contour point extraction is carried out on the semantic target; and mapping the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map. According to the method, an rgb camera installed in an overlook mode is used for detecting semantic information of the ground, coordinates of the semantic information are mapped to a robot map coordinate system, the purposes of robot obstacle avoidance navigation and positioning are achieved, obstacles on the ground can be accurately recognized, and the method is scientific and effective; and meanwhile, black and other light absorption materials can be recognized without omission, the application range is wide, and the effect is good.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent robots, and in particular to a coordinate mapping method, device, electronic device and medium. Background Art

[0002] With the development of science and technology, robots, especially indoor robots, have been widely used. In the process of robot application, its safety issues have also become a key research topic in the industry. Indoor robots mainly rely on lasers and depth cameras to avoid obstacles. Due to price reasons, the existing technology uses 2D laser radar, which can only collect planar obstacles at its installation height, and cannot detect those on the ground. The depth camera can detect ground obstacles when installed from above, but it is limited by the technical principles of the depth camera. For example, TOF will generate noise such as multipath interference, the structured light solution cannot effectively identify light-absorbing materials such as black, and the limitations of point cloud accuracy and resolution will miss small obstacles, etc. Summary of the invention

[0003] The present invention provides a coordinate mapping method, device, electronic device and medium, including: segmenting the ground area according to the installation posture of the camera; recognizing the semantic target of the segmented ground area image; extracting the contour points of the semantic target; mapping the contour point pixels to the robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the present invention uses an RGB camera installed in a top-down manner to detect the semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can recognize light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0004] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0005] A first aspect of the present disclosure provides a coordinate mapping method, comprising:

[0006] The ground area is segmented according to the camera installation posture.

[0007] Perform semantic object recognition on the segmented ground area image.

[0008] Contour points are extracted from the semantic target.

[0009] The contour point pixels are mapped to the robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0010] Furthermore, the coordinate mapping method maps the contour point pixels to the robot map coordinate system, including:

[0011] Compute lamda scale information.

[0012] Calculate the X and Y values ​​in the map coordinate system.

[0013] Furthermore, the coordinate mapping method, before calculating the lamda scale information, further includes:

[0014] The contour points are preprocessed.

[0015] Furthermore, in the coordinate mapping method, the preprocessing includes:

[0016] The preprocessing includes dedistortion and normalization.

[0017] Furthermore, in the coordinate mapping method, the camera includes:

[0018] The camera is an RGB camera.

[0019] Furthermore, in the coordinate mapping method, the RGB camera comprises:

[0020] The RGB camera includes an RGB single module and an RGB camera in an RGBD camera.

[0021] Furthermore, in the coordinate mapping method, the camera installation posture includes:

[0022] The camera is installed in a top-down position.

[0023] Furthermore, the coordinate mapping method extracts contour points of the semantic target, including:

[0024] The contour point extraction is performed through color information or edge gradient information.

[0025] Furthermore, the coordinate mapping method calculates the lamda scale information, including:

[0026] Calculate the lamda scale information λ:

[0027]

[0028] In the formula, is a semantic pixel The position of the corresponding target object in the map coordinate system, R is the rotation matrix of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, t is the translation vector of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, P u is the semantic pixel Project to a normalized coordinate system.

[0029] Furthermore, the coordinate mapping method calculates the X and Y values ​​in the map coordinate system, including:

[0030] Calculate X in map coordinates:

[0031] X=RP u [0]×λ+t[0]

[0032] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Projected to the normalized coordinate system, λ is the scale information, and t[0] is the horizontal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system.

[0033] Calculate Y in the map coordinate system:

[0034] Y=RP u [1]×λ+t[1]

[0035] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Projected to the normalized coordinate system, λ is the scale information, and t[1] is the longitudinal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system.

[0036] A second aspect of the present disclosure provides a coordinate mapping device, comprising:

[0037] The segmentation unit is used to segment the ground area according to the camera installation posture.

[0038] The recognition unit is used to perform semantic target recognition on the segmented ground area image.

[0039] The extraction unit is used to extract contour points of the semantic target.

[0040] A mapping unit is used to map the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0041] Furthermore, in the coordinate mapping device, the mapping unit includes:

[0042] The first calculation module is used to calculate the lamda scale information.

[0043] The second calculation module is used to calculate the X and Y values ​​in the map coordinate system.

[0044] Furthermore, the coordinate mapping device further includes:

[0045] The preprocessing module is used to preprocess the contour points.

[0046] A third aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0047] A fourth aspect of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0048] The present invention provides a coordinate mapping method, device, electronic device and medium, including: segmenting the ground area according to the installation posture of the camera; recognizing the semantic target of the segmented ground area image; extracting the contour points of the semantic target; mapping the contour point pixels to the robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the present invention uses an RGB camera installed in a top-down manner to detect the semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can recognize light-absorbing materials such as black without omission, and has a wide range of applications and good effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A schematic diagram of a coordinate mapping method flow in an embodiment of the present disclosure;

[0051] Figure 2 This is a schematic diagram of another coordinate mapping method flow in an embodiment of the present disclosure;

[0052] Figure 3 A schematic diagram of the structure of a coordinate mapping device in an embodiment of the present disclosure;

[0053] Figure 4It is a schematic diagram of the structure of another coordinate mapping device in an embodiment of the present disclosure;

[0054] Figure 5 The present invention is a schematic diagram of the structure of a coordinate mapping electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0056] Unless otherwise defined, all technical and scientific terms used in the present disclosure have the same meanings as those commonly understood by technicians in the field of the present disclosure; the terms used in the present disclosure are only for the purpose of describing specific embodiments, and are not intended to limit the terms "including" and "having" and any variations thereof in the specification and claims of the present disclosure and the above-mentioned drawings, and are intended to cover non-exclusive inclusions.

[0057] In the description of the embodiments of the present disclosure, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present disclosure, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0058] In the description of the embodiments of the present disclosure, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0059] In the description of the embodiments of the present disclosure, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0060] In the description of the embodiments of the present disclosure, the orientations or positional relationships indicated by technical terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", and "circumferential" are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing the embodiments of the present disclosure and simplifying the description. They do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the embodiments of the present disclosure.

[0061] In the description of the embodiments of the present disclosure, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present disclosure can be understood according to the specific circumstances.

[0062] Example 1

[0063] The present disclosure provides a coordinate mapping method. Figure 1 As shown, including:

[0064] 101. Segment the ground area based on the camera installation posture.

[0065] Region segmentation: The image region of interest is segmented based on the camera installation angle. For example, if the camera is installed in a bird's-eye view, the approximate area of ​​the ground in the image is as follows. Based on this prior knowledge, the image is captured in advance.

[0066] 102. Perform semantic target recognition on the segmented ground area image.

[0067] Ground semantic information: represents all useful target objects on the ground that can be used for robot obstacle avoidance and positioning, such as black and yellow lines, ground materials, objects placed on the ground (shoes, power strips, etc.), garbage, water stains, etc. The ground semantic information recognition method adopts a combination of deep learning and traditional image processing methods.

[0068] 103. Extract contour points of the semantic target.

[0069] Semantic contour point extraction: According to the distribution of the semantic area histogram, choose whether to extract contour points through color information or edge gradient information. If the color histogram is evenly distributed, extract it based on edge gradient information, otherwise perform color boundary information segmentation to save resource consumption. The image is mean blurred once, and the main color is found in the HSV color space. Since there are objects of similar colors in the environment, there will be a lot of noise points. The irrelevant noise points are removed through open-cap and closed-cap operations, and then the scattered small blocks are connected through a larger kernel expansion based on resolution. The semantic block area has been found through the color operation, and then clustered through the meanshift algorithm to determine the final semantic boundary. Regardless of whether it is through color or gradient, the final output semantic contour point is a group of pixels.

[0070] 104. Map the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0071] The disclosed embodiment provides a coordinate mapping method, including: segmenting a ground area according to the camera installation posture; performing semantic target recognition on the segmented ground area image; extracting contour points of the semantic target; mapping the contour point pixels to a robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment uses an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can identify light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0072] Example 2

[0073] The present disclosure provides a coordinate mapping method. Figure 2 As shown, including:

[0074] 201. Segment the ground area based on the camera installation posture.

[0075] The camera is an RGB camera, which includes an RGB single module and an RGB camera in an RGBD camera. The camera is installed in a top-down position.

[0076] 202. Perform semantic target recognition on the segmented ground area image.

[0077] 203. Extract contour points of the semantic target.

[0078] The contour point extraction is performed through color information or edge gradient information.

[0079] 204. Map the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0080] 2041. Preprocess the contour points.

[0081] Specifically, preprocessing includes dedistortion and normalization.

[0082] 2042. Calculate lamda scale information.

[0083] Calculate the lamda scale information λ:

[0084]

[0085] In the formula, is a semantic pixel The position of the corresponding target object in the map coordinate system, R is the rotation matrix of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, t is the translation vector of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, P u is the semantic pixel Project to a normalized coordinate system.

[0086] 2043. Calculate the X and Y values ​​in the map coordinate system.

[0087] Calculate X in map coordinates:

[0088] X=RP u [0]×λ+t[0]

[0089] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Project to the normalized coordinate system, λ is the scale information, t[0] is the horizontal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system;

[0090] Calculate Y in the map coordinate system:

[0091] Y=RP u [1]×λ+t[1]

[0092] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Projected to the normalized coordinate system, λ is the scale information, and t[1] is the longitudinal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system.

[0093] The disclosed embodiment provides a coordinate mapping method, including: segmenting a ground area according to the camera installation posture; performing semantic target recognition on the segmented ground area image; extracting contour points of the semantic target; mapping the contour point pixels to a robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment uses an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can identify light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0094] Example 3

[0095] The present disclosure provides a coordinate mapping device, such as Figure 3 As shown, including:

[0096] The segmentation unit 31 is used to segment the ground area according to the camera installation posture.

[0097] The recognition unit 32 is used to perform semantic object recognition on the segmented ground area image.

[0098] The extraction unit 33 is used to extract contour points of the semantic target.

[0099] The mapping unit 34 is used to map the contour point pixels to the robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0100] It should be noted here that: for the detailed description of each component structure in this embodiment, reference can be made to the corresponding parts of other embodiments, which will not be repeated here.

[0101] The disclosed embodiment provides a coordinate mapping device, comprising: a segmentation unit, which segments a ground area according to a camera installation posture; a recognition unit, which recognizes semantic targets on the segmented ground area image; an extraction unit, which extracts contour points of the semantic targets; a mapping unit, which maps the contour point pixels to a robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment uses an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can recognize light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0102] Example 4

[0103] The present disclosure provides a coordinate mapping device, such as Figure 4 As shown, including:

[0104] The segmentation unit 41 is used to segment the ground area according to the camera installation posture;

[0105] The recognition unit 42 is used to perform semantic target recognition on the segmented ground area image;

[0106] An extraction unit 43, used for extracting contour points of the semantic target;

[0107] The mapping unit 44 is used to map the contour point pixels to the robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0108] The preprocessing module 441 is used to preprocess the contour points.

[0109] A first calculation module 442, used to calculate lamda scale information;

[0110] The second calculation module 443 is used to calculate the X and Y values ​​in the map coordinate system.

[0111] It should be noted here that: for the detailed description of each component structure in this embodiment, reference can be made to the corresponding parts of other embodiments, which will not be repeated here.

[0112] The disclosed embodiment provides a coordinate mapping device, comprising: a segmentation unit, which segments a ground area according to a camera installation posture; a recognition unit, which recognizes semantic targets on the segmented ground area image; an extraction unit, which extracts contour points of the semantic targets; a mapping unit, which maps the contour point pixels to a robot map coordinate system, so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment uses an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can recognize light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0113] Example 5

[0114] The present disclosure provides a coordinate mapping electronic device, such as Figure 5 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned coordinate mapping method when executing the computer program.

[0115] A processing device (e.g., a central processing unit, a graphics processing unit, etc.) 51 can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 52 or a program loaded from a storage device 58 into a random access memory (RAM) 53. Various programs and data required for the operation of the electronic device are also stored in the RAM 53. The processing device 51, the ROM 52, and the RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0116] Typically, the following devices may be connected to the I / O interface 55: an input device 56 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 57 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 58 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 59. The communication device 59 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 5 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0117] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 59, or installed from the storage device 58, or installed from the ROM 52. When the computer program is executed by the processing device 51, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0118] It should be noted here that: for the detailed description of each component structure in this embodiment, reference can be made to the corresponding parts of other embodiments, which will not be repeated here.

[0119] The disclosed embodiment provides a coordinate mapping electronic device, comprising: segmenting a ground area according to a camera installation posture; identifying semantic targets on the segmented ground area image; extracting contour points of the semantic targets; mapping the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment utilizes an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of robot obstacle avoidance navigation and positioning, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can identify light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0120] Example 6

[0121] An embodiment of the present disclosure provides a computer-readable storage medium for coordinate mapping, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by one or more processors, the above-mentioned coordinate mapping method is implemented.

[0122] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0123] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0124] The computer-readable medium may be included in the device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: performs ground area segmentation according to the camera installation posture; performs semantic target recognition on the ground area image after segmentation; extracts contour points of the semantic target; and maps the contour point pixels to the robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

[0125] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] It should be noted here that: for the detailed description of each component structure in this embodiment, reference can be made to the corresponding parts of other embodiments, which will not be repeated here.

[0127] The disclosed embodiment provides a coordinate mapping computer-readable storage medium, comprising: segmenting a ground area according to a camera installation posture; performing semantic target recognition on the segmented ground area image; extracting contour points of the semantic target; mapping the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map. Compared with the prior art, the disclosed embodiment utilizes an RGB camera installed in a top-down manner to detect semantic information of the ground, and maps the coordinates of the semantic information to the robot map coordinate system, thereby achieving the purpose of obstacle avoidance navigation and positioning of the robot, and can accurately identify obstacles on the ground, which is scientific and effective; at the same time, it can identify light-absorbing materials such as black without omission, and has a wide range of applications and good effects.

[0128] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a receiving unit, an information acquisition unit, a target determination unit, and a distribution unit. The names of these units do not constitute limitations on the units themselves in some cases.

[0130] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0131] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in each embodiment can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A coordinate mapping method, characterized in that: include: Segment the ground area based on the camera installation position; Perform semantic target recognition on the segmented ground area image; Extracting contour points of the semantic target; The contour point pixels are mapped to the robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

2. The coordinate mapping method according to claim 1, characterized in that: Mapping the contour point pixels to the robot map coordinate system includes: Calculate lamda scale information; Calculate the X and Y values ​​in the map coordinate system.

3. The coordinate mapping method according to claim 2, characterized in that: Before calculating the lamda scale information, it also includes: The contour points are preprocessed.

4. The coordinate mapping method according to claim 3, characterized in that: The pre-processing comprises: The preprocessing includes dedistortion and normalization.

5. The coordinate mapping method according to claim 1, characterized in that: The camera comprises: The camera is an RGB camera.

6. The coordinate mapping method according to claim 5, characterized in that: The RGB camera comprises: The RGB camera includes an RGB single module and an RGB camera in an RGBD camera.

7. The coordinate mapping method according to claim 1, characterized in that: The camera installation posture includes: The camera is installed in a top-down position.

8. The coordinate mapping method according to claim 1, characterized in that: Extracting contour points of the semantic target includes: The contour point extraction is performed through color information or edge gradient information.

9. The coordinate mapping method according to claim 2, characterized in that: Calculate lamda scale information, including: Calculate the lamda scale information λ: In the formula, is a semantic pixel The position of the corresponding target object in the map coordinate system, R is the rotation matrix of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, t is the translation vector of the product of the pose from the camera to the robot coordinates and the pose from the robot to the map coordinate system, P u is the semantic pixel Project to a normalized coordinate system.

10. The coordinate mapping method according to claim 2, characterized in that: Calculate the X and Y values ​​in the map coordinate system, including: Calculate X in map coordinates: X=RP u [0]×λ+t[0] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Project to the normalized coordinate system, λ is the scale information, t[0] is the horizontal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system; Calculate Y in the map coordinate system: Y=RP u [1]×λ+t[1] Where R is the rotation matrix of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system, P u is the semantic pixel Projected to the normalized coordinate system, λ is the scale information, and t[1] is the longitudinal translation vector of the product of the pose from the camera to the robot coordinate system and the pose from the robot to the map coordinate system.

11. A coordinate mapping device, characterized in that: include: A segmentation unit is used to segment the ground area according to the camera installation posture; A recognition unit, used for performing semantic target recognition on the segmented ground area image; An extraction unit, used for extracting contour points of the semantic target; A mapping unit is used to map the contour point pixels to a robot map coordinate system so that the robot can perform obstacle avoidance navigation and positioning according to the map.

12. The coordinate mapping device according to claim 11, characterized in that: The mapping unit comprises: The first calculation module is used to calculate the lamda scale information; The second calculation module is used to calculate the X and Y values ​​in the map coordinate system.

13. The coordinate mapping device according to claim 11, characterized in that: Also includes: The preprocessing module is used to preprocess the contour points.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

15. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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