Indoor Robot Navigation and Positioning Method, System, Electronic Device and Storage Medium

Abstract

The present invention provides an indoor robot navigation and positioning method, system, electronic device and storage medium. The method includes: acquiring initial point cloud data of the indoor bottom and image data of the indoor top collected by the robot; constructing a bottom environment map according to the initial point cloud data, and constructing a top feature map according to the image data; during the navigation movement of the robot according to the bottom environment map, acquiring real-time point cloud data, comparing the real-time point cloud data with the initial point cloud data for similarity to obtain a first similarity comparison value; selecting the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data; updating the navigation path according to the navigation data and the real-time point cloud data, and controlling the movement of the robot according to the navigation path. The present invention can achieve more accurate environmental perception and map construction, improve the navigation and positioning accuracy of the robot, and enhance the autonomy and adaptability of the robot.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and particularly to an indoor robot navigation and positioning method, system, electronic device and storage medium. Background Art

[0002] With the development of science and technology and the improvement of people's living standards, service robots have gradually integrated into people's daily lives, providing people with rich and user-friendly services. Compared with other types of robots, service robots usually operate in more complex environments, which poses higher requirements for their positioning capabilities. Positioning is to determine the coordinates of the robot in the world coordinate system of its motion environment, which is crucial for service robots to perform various tasks. Accurate positioning information can help the robot understand its own position, the surrounding environment and the target position, so as to achieve precise navigation, obstacle avoidance and task completion.

[0003] When a service robot operates in an indoor environment, it mainly relies on a bottom lidar for positioning and navigation. The lidar calculates the distance between an object and the robot by emitting laser beams and measuring the time it takes for the laser beams to reflect back. As one of the main sensors in service robots, the lidar can continuously scan the surrounding environment, obtain high-precision distance information, and form initial point cloud data. These data are then used to construct a map of the environment where the robot is located. However, the movement of indoor objects or changes in the environment (such as the rearrangement of furniture, the appearance of temporary obstacles, etc.) may cause the robot to be unable to accurately match the previously created map, resulting in positioning errors and "getting lost" phenomena, which directly affect the navigation accuracy and operation efficiency of service robots. Summary of the Invention

[0004] In view of the above deficiencies of the prior art, the present invention provides an indoor robot navigation and positioning method, system, electronic device and storage medium, which effectively solves the problems of low navigation accuracy and operation efficiency of service robots in indoor scenarios.

[0005] In a first aspect, the present invention provides an indoor robot navigation and positioning method, the method comprising:

[0006] Obtaining initial point cloud data at the bottom of the indoor area collected by the robot and image data at the top of the indoor area;

[0007] Constructing a bottom environment map according to the initial point cloud data, and constructing a top feature map according to the image data;

[0008] During the process of the robot navigating and moving according to the bottom environment map, obtaining real-time point cloud data, comparing the similarity between the real-time point cloud data and the initial point cloud data to obtain a first similarity comparison value;

[0009] Select the bottom environmental map and / or the top feature map for navigation according to the first similarity comparison value, and obtain navigation data;

[0010] Update the navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0011] Further, the step of selecting the bottom environmental map and / or the top feature map for navigation according to the first similarity comparison value and obtaining navigation data includes:

[0012] If the first similarity comparison value is greater than or equal to the first set threshold, use the bottom environmental map for navigation to obtain the navigation data;

[0013] If the first similarity comparison value is less than the first set threshold and greater than the second set threshold, use the top feature map in combination with the bottom environmental map for navigation to obtain the navigation data;

[0014] If the first similarity comparison value is less than the second set threshold, the robot stops running and issues an alarm message.

[0015] Further, after using the top feature map in combination with the bottom environmental map for navigation, it further includes:

[0016] Compare the similarity between the real-time point cloud data and the data of the top feature map to obtain a second similarity comparison value;

[0017] If the second similarity comparison value is greater than or equal to the third set threshold, use the top feature map in combination with the bottom environmental map for navigation to obtain the navigation data;

[0018] If the second similarity comparison value is less than the third set threshold, the robot stops running and issues an alarm message.

[0019] Further, the step of obtaining the initial point cloud data at the bottom of the indoor environment and the image data at the top of the indoor environment collected by the robot includes:

[0020] Control the laser acquisition device of the robot to emit a laser beam, receive the laser reflected by the object at the bottom of the indoor environment, calculate the time difference between the emission and reception of the laser beam, and construct a global map;

[0021] Record the emission angle and distance information of the laser beam, and calculate the time difference between the emission and reception of the laser beam to form the initial point cloud data;

[0022] Control the image acquisition device of the robot to acquire the indoor top image, extract the grayscale value according to the indoor top image, and obtain the image data.

[0023] Further, the constructing the bottom environment map according to the initial point cloud data includes:

[0024] Preprocess the initial point cloud data of the current frame and the previous frame, and extract feature points;

[0025] Perform feature matching on the initial point cloud data of the current frame and the previous frame according to the feature points, and calculate the pose data of the robot from the previous frame to the current frame;

[0026] Optimize the initial point cloud data according to the pose data, fuse the optimized initial point cloud data into the global map, and construct the bottom environment map.

[0027] Further, the fusing the optimized initial point cloud data into the global map to construct the bottom environment map includes:

[0028] Register the optimized initial point cloud data with the global map;

[0029] Determine the overlapping area and non-overlapping area between the initial point cloud data and the global map by the Euclidean distance method;

[0030] Perform weighted fusion on the initial point cloud data in the overlapping area to obtain fusion data, and perform segmentation and screening on the non-overlapping area by the clustering method to obtain environmental information;

[0031] Construct the bottom environment map according to the fusion data and the environmental information.

[0032] Further, the constructing the top feature map according to the image data includes:

[0033] Extract features from the image data to obtain key feature information;

[0034] Segment the image data to obtain multiple image region objects;

[0035] Perform edge detection on multiple image region objects to obtain edge feature information;

[0036] Construct the top feature map according to the key feature information and the edge feature information.

[0037] In a second aspect, the present invention provides an indoor robot navigation and positioning system, and the system includes:

[0038] A data acquisition module, configured to acquire the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot;

[0039] A map construction module, configured to construct a bottom environment map according to the initial point cloud data and construct a top feature map according to the image data;

[0040] A data comparison module, configured to acquire real-time point cloud data during the navigation movement of the robot according to the bottom environment map, compare the real-time point cloud data with the initial point cloud data, and obtain a first similarity comparison value;

[0041] A map selection module, configured to select the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value, and acquire navigation data;

[0042] A navigation and positioning module, configured to update a navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0043] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the indoor robot navigation and positioning method as described in the first aspect of the present invention.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the indoor robot navigation and positioning method as described in the first aspect of the present invention is implemented.

[0045] The indoor robot navigation and positioning method, system, electronic device, and storage medium provided by the present invention integrate the point cloud data of the indoor bottom and the image data of the indoor top. When there are differences in the comparison of the bottom point cloud data, the robot will use the top feature information for comparison and fusion, adjust the position estimation and map information of the robot through an optimization algorithm, and realize the real-time correction of the map. Thus, the robot can more accurately perceive the changes in the surrounding environment, adapt to complex situations such as the movement of indoor items or environmental changes, realize more accurate environmental perception and map construction, and improve the navigation and positioning accuracy of the robot. At the same time, the corrected map not only provides a more reliable reference for future navigation tasks but also enhances the autonomy and adaptability of the robot. Description of the Drawings

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0047] Figure 1 It is the first schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0048] Figure 2 It is the second schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0049] Figure 3 It is the third schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0050] Figure 4 It is the fourth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0051] Figure 5 It is the fifth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0052] Figure 6 It is the sixth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0053] Figure 7 It is the seventh schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention;

[0054] Figure 8 It is the schematic diagram of the indoor robot navigation and positioning system structure provided by the embodiment of the present invention;

[0055] Figure 9 It is the schematic diagram of the structure of an electronic device provided by the embodiment of the present invention.

[0056] Main element symbol description:

[0057] 800, indoor robot navigation and positioning system; 810, data acquisition module; 820, map construction module; 830, data comparison module; 840, map selection module; 850, navigation and positioning module; 900, electronic device; 910, processor; 920, communication interface; 930, memory; 940, communication bus. Specific embodiments

[0058] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be further described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the scope of protection of the present invention.

[0059] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0061] When a service robot operates in an indoor environment, it mainly relies on a bottom lidar for positioning and navigation. The lidar calculates the distance between an object and the robot by emitting a laser beam and measuring the time it takes for the beam to reflect back. As one of the main sensors in a service robot, the lidar can continuously scan the surrounding environment, obtain high-precision distance information, and form initial point cloud data. These data are then used to construct a map of the environment where the robot is located. However, the movement of indoor items or changes in the environment (such as the rearrangement of furniture or the appearance of temporary obstacles) may cause the robot to be unable to accurately match the previously created map, resulting in positioning errors and "getting lost" phenomena, which directly affect the navigation accuracy and operation efficiency of the service robot.

[0062] Embodiment 1

[0063] In view of the above deficiencies of the prior art, the embodiments of the present invention provide an indoor robot navigation and positioning method, which effectively solves the problems of low navigation accuracy and operation efficiency of service robots in indoor scenarios. Figure 1 It is the first schematic diagram of the flow of the indoor robot navigation and positioning method provided by the embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0064] S100. Obtain the initial point cloud data at the bottom of the indoor area collected by the robot and the image data at the top of the indoor area;

[0065] In an embodiment of the present invention, the robot includes, but is not limited to, indoor robots such as food delivery robots, home service robots, warehousing and logistics robots, and medical assistance robots. A laser acquisition device is provided at the bottom of the robot for scanning the indoor bottom environment to obtain environmental data of the indoor bottom. An image acquisition device is provided at the top of the robot for scanning the indoor top environment to obtain top feature data.

[0066] Figure 2 This is the second schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention. As Figure 2 shown, step S100 specifically includes the following steps:

[0067] S110. Control the laser acquisition device of the robot to emit a laser beam, and receive the laser reflected by an object at the indoor bottom. Calculate the time difference between the emission and reception of the laser beam to construct a global map.

[0068] In an embodiment of the present invention, the laser acquisition device can be a lidar. The lidar emits laser beams at a certain frequency through a laser light source. The laser reception module of the lidar is responsible for receiving the laser reflected by the object. The laser data processing module of the lidar calculates the time difference or phase difference between the emission and reception of the laser beam, and constructs a global map through the SLAM algorithm, and calculates the position and orientation of the robot. This is a two-dimensional plane map containing information about obstacles and passable areas.

[0069] S120. Record the emission angle and distance information of the laser beam to form initial point cloud data;

[0070] At the same time, the lidar scans the surrounding environment by rotating and swinging. During the scanning process, the lidar continuously records the emission angle and corresponding distance information of each laser beam to form a series of initial point cloud data. These initial point cloud data contain information such as the position, shape, and size of obstacles in the environment.

[0071] S130. Control the image acquisition device of the robot to acquire an indoor top image, extract the grayscale value according to the indoor top image, and obtain image data.

[0072] In an embodiment of the present invention, the image acquisition device may be a camera, which is used to capture features on the top of the room (such as ceiling structure, chandelier, etc.). The light is focused on the image sensor CMOS through the optical lens of the camera. In this process, the optical signal is converted into an analog electrical signal, and then the analog electrical signal is converted into a digital signal by an analog-to-digital converter ADC. The digital signal represents the image in the form of pixels, and each pixel contains color information (red, green, blue, i.e., RGB), obtaining a digital signal RGB image. The RGB values in the digital signal RGB image are weighted and averaged through a floating-point algorithm to obtain a grayscale value. The calculation formula of the floating-point algorithm is as follows:

[0073] Gray = R×0.3 + G×0.59 + B×0.11

[0074] In the above formula, Gray represents the grayscale value, R represents the channel value of red, G represents the channel value of green, and B represents the channel value of white.

[0075] The grayscale value is an integer value between 0 (black) and 255 (white), which represents the brightness of each pixel in the image. The captured image is usually in color, but when constructing the top feature map of the room, only the brightness information of the image is extracted, and color information is not required. The top feature map can be constructed through the grayscale value. At the same time, the data volume of the grayscale value is small, which can improve the speed of later data operation and data comparison, and reduce the error probability.

[0076] S200. Construct a bottom environment map based on the initial point cloud data, and construct a top feature map based on the image data.

[0077] Figure 3 is the third schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention. As Figure 3 shown, constructing the bottom environment map includes the following steps:

[0078] S210. Preprocess the initial point cloud data of the current frame and the previous frame, and extract feature points.

[0079] Perform preprocessing operations such as filtering and denoising on the initial point cloud data of the current frame and the previous frame or multiple previous frames, and then extract feature points from the preprocessed initial point cloud data.

[0080] S220. Feature-match the initial point cloud data of the current frame and the previous frame according to the feature points, and calculate the pose data of the robot from the previous frame to the current frame.

[0081] Use brute-force matching and the FLANN algorithm to feature-match the initial point cloud data of the current frame with the initial point cloud data of the previous frame or multiple previous frames, and calculate the preliminary pose transformation of the robot from the previous frame to the current frame based on the matched features.

[0082] S230. Optimize the initial point cloud data according to the pose data, fuse the optimized initial point cloud data into the global map, and construct the bottom environment map.

[0083] Obtain multiple frames of initial point cloud data for pose optimization to improve accuracy, fuse the optimized initial point cloud data of the current frame into the global map, and construct the bottom environment map. Figure 4 It is the fourth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention. As Figure 4 shown, step S230 specifically includes the following steps:

[0084] S231. Register the optimized initial point cloud data with the global map.

[0085] In the embodiment of the present invention, the Super4pcs algorithm is used to register the optimized initial point cloud data with the global map to ensure their consistency in spatial position.

[0086] S232. Determine the overlapping area and non-overlapping area between the initial point cloud data and the global map through the Euclidean distance method.

[0087] S233. Perform weighted fusion on the initial point cloud data in the overlapping area to obtain fusion data, and perform segmentation and screening on the non-overlapping area through the clustering method to obtain environmental information.

[0088] Perform weighted fusion on the initial point cloud data in the overlapping area to ensure smooth transition of the fused data. The non-overlapping area is segmented and screened through the clustering method to retain useful environmental information.

[0089] S234. Construct the bottom environment map according to the fusion data and environmental information.

[0090] Continuously obtain the optimized initial point cloud data, repeat point cloud registration, fusion, and optimization, and gradually construct a complete bottom environment map.

[0091] Figure 5 It is the fifth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention. As Figure 5 shown, constructing the top feature map specifically includes the following steps:

[0092] S240. Extract features according to the image data to obtain key feature information.

[0093] Feature extraction is the process of identifying key information useful for constructing the map in the image. In a grayscale image, these features may include texture, shape, color (brightness change in a grayscale image), etc.

[0094] S250. Segment the image data to obtain multiple image region objects.

[0095] Image segmentation is the process of dividing an image into multiple regions or objects that are visually or statistically similar. In the embodiments of the present invention, methods such as threshold segmentation, region growing algorithm, level set method, and graph cut algorithm can be used for image segmentation.

[0096] S260. Perform edge detection on the multiple image region objects to obtain edge feature information.

[0097] Edge detection is the process of identifying positions with significant brightness changes in an image, which usually correspond to the boundaries of objects. In the embodiments of the present invention, the Canny edge detection algorithm or the Sobel edge detection algorithm is used for edge detection.

[0098] S270. Construct a top feature map based on the key feature information and the edge feature information.

[0099] Based on the key feature information and the edge feature information, geocoding, topological relationship analysis, vectorization, and map rendering are used to construct a top feature map.

[0100] S300. During the navigation movement of the robot according to the bottom environmental map, obtain real-time point cloud data, compare the real-time point cloud data with the initial point cloud data for similarity, and obtain a first similarity comparison value.

[0101] During the movement of the robot, the bottom lidar is used as the main navigation basis, and the bottom environmental map is used for real-time positioning and navigation during movement. Calculate the similarity between the real-time point cloud data and the initial point cloud data of the bottom environmental map to obtain a first similarity comparison value.

[0102] S400. Select the bottom environmental map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data.

[0103] First, set the similarity switching threshold of the point cloud data of the lidar as the first set threshold, the lidar data similarity alarm threshold as the second set threshold, and the alarm threshold of the camera as the third set threshold, which are used to determine whether to switch the navigation strategy and the alarm strategy.

[0104] Figure 6 It is the sixth schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiments of the present invention. As Figure 6 shown, step S400 specifically includes the following steps:

[0105] S410. If the first similarity comparison value is greater than or equal to the first set threshold, use the bottom environmental map for navigation to obtain navigation data.

[0106] S420. If the first similarity comparison value is less than the first set threshold and greater than the second set threshold, then use the top feature map combined with the bottom environment map for navigation to obtain navigation data.

[0107] Specifically, extract stable visual features (such as corner points, edges, textures, etc.) from the top feature map, compare the extracted features with the top features pre-stored in the map, and find the matching points. These matching points can be used as "anchor points" to correct the map error caused by environmental changes and help the robot to be more accurately positioned on the bottom environment map of the environment. By combining the results of the bottom point cloud data and the top image feature matching, adjust the position estimation and map information of the robot through an optimization algorithm, so as to realize the real-time correction of the map.

[0108] When the similarity of the point cloud data of the lidar data resumes above the first set threshold, switch back to the lidar-based navigation mode and use the bottom environment map for navigation.

[0109] S430. If the first similarity comparison value is less than the second set threshold, the robot stops running and issues an alarm message.

[0110] At this time, the robot may be running on an unfamiliar map and needs to re-scan the environment through the lidar and camera to create the corresponding map.

[0111] Figure 7 It is the seventh schematic diagram of the indoor robot navigation and positioning method flow provided by the embodiment of the present invention. As Figure 7 shown, step S400 further includes the following steps:

[0112] S440. Compare the similarity between the real-time point cloud data and the data of the top feature map to obtain a second similarity comparison value.

[0113] S450. If the second similarity comparison value is greater than or equal to the third set threshold, use the top feature map combined with the bottom environment map for navigation to obtain navigation data.

[0114] S460. If the second similarity comparison value is less than the third set threshold, the robot stops running and issues an alarm message.

[0115] Similarly, at this time, the robot may be running on an unfamiliar map and needs to re-scan the environment through the lidar and camera to create the corresponding map.

[0116] S500. Update the navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0117] Based on the acquired navigation data and real-time point cloud data, the robot can make more accurate navigation decisions to update the navigation path, such as selecting the optimal path, avoiding obstacles, etc. By transmitting the navigation path to the hardware control unit, the movement of the robot is controlled.

[0118] The indoor robot navigation and positioning method provided by the embodiments of the present invention integrates the point cloud data at the bottom of the indoor environment and the image data at the top of the indoor environment. When there are differences in the comparison of the bottom point cloud data, the robot will use the top feature information for comparison and fusion, and adjust the position estimation and map information of the robot through an optimization algorithm to achieve real-time correction of the map. Thus, the robot can more accurately perceive the changes in the surrounding environment and adapt to complex situations such as the movement of indoor items or environmental changes.

[0119] Embodiment 2

[0120] Based on the same technical concept as the method embodiment of Embodiment 1 above, the embodiments of the present invention provide an indoor robot navigation and positioning system. Figure 8 It is a schematic structural diagram of the indoor robot navigation and positioning system provided by the embodiments of the present invention. As Figure 8 shown, the indoor robot navigation and positioning system 800 includes:

[0121] A data acquisition module 810, configured to acquire the initial point cloud data at the bottom of the indoor environment and the image data at the top of the indoor environment collected by the robot;

[0122] A map construction module 820, configured to construct a bottom environment map according to the initial point cloud data and construct a top feature map according to the image data;

[0123] A data comparison module 830, configured to acquire real-time point cloud data during the navigation movement of the robot according to the bottom environment map, compare the real-time point cloud data with the initial point cloud data, and obtain a first similarity comparison value;

[0124] A map selection module 840, configured to select the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data;

[0125] A navigation and positioning module 850, configured to update the navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0126] The indoor robot navigation and positioning system provided by the embodiments of the present invention can achieve more accurate environmental perception and map construction, improve the navigation and positioning accuracy of the robot. At the same time, the corrected map not only provides a more reliable reference for future navigation tasks, but also enhances the autonomy and adaptability of the robot.

[0127] It can be understood that the implementation manners in the indoor robot navigation and positioning method described in the above Embodiment 1 are equally applicable to this embodiment and can achieve the same technical effects, so they will not be repeated here.

[0128] Embodiment 3

[0129] Based on the same concept, an embodiment of the present invention further provides an electronic device. Figure 9 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 9 shown, the electronic device 900 may include: a processor 910, a communications interface 920, a memory 930, and a communication bus 940. Among them, the processor 910, the communications interface 920, and the memory 930 communicate with each other through the communication bus 940. The processor 910 may call logic instructions in the memory 930 to execute the steps of the indoor robot navigation and positioning method described in the above embodiments. For example, it includes:

[0130] S100. Obtain the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot;

[0131] S200. Construct a bottom environment map according to the initial point cloud data and construct a top feature map according to the image data;

[0132] S300. During the navigation movement of the robot according to the bottom environment map, obtain real-time point cloud data, compare the real-time point cloud data with the initial point cloud data for similarity, and obtain a first similarity comparison value;

[0133] S400. Select the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data;

[0134] S500. Update the navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0135] Among them, the processor 910 can be a Central Processing Unit (CPU). The processor can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above various types of chips.

[0136] In addition, when the logical instructions in the above-mentioned memory 930 can be implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, Read-Only Memories (ROMs), Random Access Memories (RAMs), magnetic disks, or optical discs, etc., various media that can store program codes.

[0137] The memory 930 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and their combinations.

[0138] Embodiment 4

[0139] Based on the same concept, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program includes at least one segment of code. The at least one segment of code can be executed by a main control device to control the main control device to implement the steps of the indoor robot navigation and positioning method as described in the above embodiments. For example, it includes:

[0140] S100. Obtain the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot;

[0141] S200. Construct a bottom environment map based on the initial point cloud data, and construct a top feature map based on the image data;

[0142] S300. During the process of the robot navigating and moving according to the bottom environment map, obtain real-time point cloud data, compare the real-time point cloud data with the initial point cloud data, and obtain a first similarity comparison value;

[0143] S400. According to the first similarity comparison value, select the bottom environment map and / or the top feature map for navigation to obtain navigation data;

[0144] S500. Update the navigation path according to the navigation data and the real-time point cloud data, and control the movement of the robot according to the navigation path.

[0145] Based on the same technical concept, an embodiment of the present invention further provides a computer program, which is used to implement the above method embodiment when the computer program is executed by the main control device.

[0146] The computer program can be stored in whole or in part on a computer-readable storage medium packaged together with the processor, or can be stored in whole or in part on a memory not packaged together with the processor.

[0147] Based on the same technical concept, an embodiment of the present invention further provides a processor, which is used to implement the above method embodiment. The above processor can be a chip.

[0148] In summary, for the indoor robot navigation and positioning method, system, electronic device and storage medium provided by the present invention, by integrating the point cloud data of the indoor bottom and the image data of the indoor top, when there are differences in the comparison of the point cloud data at the bottom, the robot will use the top feature information for comparison and fusion, adjust the position estimation and map information of the robot through an optimization algorithm, and realize the real-time correction of the map. Thus, the robot can more accurately perceive the changes in the surrounding environment, adapt to complex situations such as the movement of indoor items or environmental changes, realize more accurate environmental perception and map construction, and improve the navigation and positioning accuracy of the robot. At the same time, the corrected map not only provides a more reliable reference for future navigation tasks, but also enhances the autonomy and adaptability of the robot.

[0149] References to "embodiments" in this specification mean that the particular features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0150] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An indoor robot navigation and positioning method, characterized in that, The method includes: Obtaining initial point cloud data of the indoor bottom and image data of the indoor top collected by the robot; Constructing a bottom environment map based on the initial point cloud data and constructing a top feature map based on the image data; During the process of the robot navigating and moving according to the bottom environment map, obtaining real-time point cloud data, comparing the similarity between the real-time point cloud data and the initial point cloud data to obtain a first similarity comparison value; Selecting the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data; Updating the navigation path according to the navigation data and the real-time point cloud data, and controlling the movement of the robot according to the navigation path; Among them, the obtaining of the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot includes: Controlling the laser acquisition device of the robot to emit a laser beam, receiving the laser reflected by an object at the indoor bottom, calculating the time difference between the emission and reception of the laser beam, and constructing a global map; Recording the emission angle and distance information of the laser beam to form initial point cloud data; Controlling the image acquisition device of the robot to collect an indoor top image, extracting gray values from the indoor top image to obtain the image data; The constructing of the bottom environment map according to the initial point cloud data includes: Preprocessing the initial point cloud data of the current frame and the previous frame, and extracting feature points; Performing feature matching on the initial point cloud data of the current frame and the previous frame according to the feature points, and calculating the pose data of the robot from the previous frame to the current frame; Optimizing the initial point cloud data according to the pose data, and fusing the optimized initial point cloud data into the global map to construct the bottom environment map.

2. The indoor robot navigation and positioning method according to claim 1, characterized in that, The selecting of the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data includes: If the first similarity comparison value is greater than or equal to a first set threshold, using the bottom environment map for navigation to obtain the navigation data; If the first similarity comparison value is less than the first set threshold and the first similarity comparison value is greater than a second set threshold, using the top feature map in combination with the bottom environment map for navigation to obtain the navigation data; If the first similarity comparison value is less than the second set threshold, the robot stops running and sends out an alarm message.

3. The indoor robot navigation and positioning method according to claim 1, wherein, The fusing of the optimized initial point cloud data into the global map to construct the bottom environment map includes: Registering the optimized initial point cloud data with the global map; Determining the overlapping area and non-overlapping area between the initial point cloud data and the global map by the Euclidean distance method; Performing weighted fusion on the initial point cloud data in the overlapping area to obtain fusion data, and performing segmentation and screening on the non-overlapping area by a clustering method to obtain environmental information; Constructing the bottom environment map according to the fusion data and the environmental information.

4. The indoor robot navigation and positioning method according to claim 1, characterized in that The constructing of the top feature map according to the image data includes: Extract features from the image data to obtain key feature information; Perform image segmentation on the image data to obtain multiple image region objects; Perform edge detection on the multiple image region objects to obtain edge feature information; Construct the top feature map according to the key feature information and the edge feature information.

5. An indoor robot navigation and positioning system, characterized in that, The system includes: A data acquisition module for acquiring the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot; A map construction module for constructing a bottom environment map according to the initial point cloud data and constructing a top feature map according to the image data; A data comparison module for acquiring real-time point cloud data during the navigation movement of the robot according to the bottom environment map, comparing the similarity between the real-time point cloud data and the initial point cloud data, and obtaining a first similarity comparison value; A map selection module for selecting the bottom environment map and / or the top feature map for navigation according to the first similarity comparison value to obtain navigation data; A navigation and positioning module for updating the navigation path according to the navigation data and the real-time point cloud data and controlling the movement of the robot according to the navigation path; Among them, the acquisition of the initial point cloud data of the indoor bottom and the image data of the indoor top collected by the robot includes: Controlling the laser acquisition device of the robot to emit a laser beam, receiving the laser reflected by an object at the indoor bottom, calculating the time difference between the emission and reception of the laser beam, and constructing a global map; Recording the emission angle and distance information of the laser beam to form initial point cloud data; Controlling the image acquisition device of the robot to acquire an indoor top image, extracting gray values according to the indoor top image, and obtaining the image data; The construction of the bottom environment map according to the initial point cloud data includes: Preprocessing the initial point cloud data of the current frame and the previous frame and extracting feature points; Performing feature matching on the initial point cloud data of the current frame and the previous frame according to the feature points and calculating the pose data of the robot from the previous frame to the current frame; Optimizing the initial point cloud data according to the pose data, fusing the optimized initial point cloud data into the global map, and constructing the bottom environment map.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the indoor robot navigation and positioning method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the indoor robot navigation and positioning method according to any one of claims 1 to 4.

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