Map construction method, robot and computer-readable storage medium

Abstract

The present invention provides a map construction method, a robot, and a computer-readable storage medium. The robot has an original map within its range of activity, wherein the map construction method includes: controlling the robot to move to a spatial range corresponding to the original map and locating the robot's location; controlling the robot to move to a newly added area and scanning the newly added area using a distance sensor; controlling the robot to move within the newly added area, continuously scanning the newly added area using a distance sensor, constructing a map of the newly added area, and determining the robot's position relative to the original map. An embodiment of the present invention utilizes the original map within the robot's range of activity and directly constructs a map of the newly added area using a SLAM method while the robot is moving. This significantly reduces the amount of data required to be stored, eliminates the need to delete the original map, simplifies the operation process, and allows for flexible selection of loop detection positions, thereby improving the flexibility and applicability of the map construction method.

Description

Technical Field

[0001] The present invention generally relates to the field of intelligent device technology, and in particular to a map construction method, a robot, and a computer-readable storage medium. Background Art

[0002] SLAM (Simultaneous Localization and Mapping) is a map-building method widely used in the field of robotics. It controls the movement of a robot in a location environment and obtains the boundary information of the map through scanning data from distance sensors to complete the robot's positioning, navigation and map construction. It is mainly used in indoor environments such as exhibitions, restaurants, and warehouses.

[0003] When it is necessary to expand the map, the existing technology usually uses process data files, and loads the process data files when continuing to build the map to expand the map. However, in traditional methods, the process data files that need to be stored when expanding the map include maps, laser data (using lidar as a distance sensor), nodes, trajectory information, etc. The amount of data is extremely large, which creates great computing and storage pressure. In addition, for service robots, such as food delivery robots, the modifications made to the map at the interactive end can only be saved in the form of pictures, and it is difficult to save them through process data. As a result, in the subsequent mapping process, the marks reserved at the interactive end are lost and cannot be recorded. They need to be re-marked, and the operation process is cumbersome.

[0004] In addition, the SLAM method constructs maps and requires closed-loop verification of the scanning data obtained by the distance sensor. When expanding the map, relying on traditional methods, closed-loop verification can only be performed at fixed positions, which has low flexibility and cannot return to the original position in certain specific environments, making it impossible to complete the closed-loop verification process.

[0005] The contents of the background technology section are merely the technologies known to the inventors and do not necessarily represent the existing technologies in this field. Summary of the Invention

[0006] In response to one or more deficiencies in the prior art, the present invention provides a map construction method for constructing a map within the activity range of a robot, wherein the robot has a distance sensor and an original map within the activity range of the robot. The map construction method comprises:

[0007] Control the robot to move to the space corresponding to the original map and locate the robot's position;

[0008] Control the robot to move to the newly added area and use the distance sensor to scan the newly added area;

[0009] Control the robot to move in the newly added area, continuously use the distance sensor to scan the newly added area, build a map of the newly added area, and determine the robot's position relative to the original map.

[0010] According to one aspect of the present invention, the step of constructing a new area map further comprises:

[0011] Pass the original map as a subgraph into the mapping algorithm;

[0012] Adding the robot's scanning data in the newly added area as an increment to the subgraph;

[0013] The constraint relationship between the node and the subgraph and / or other nodes is calculated in real time, where the node includes the current position of the robot and / or the characteristic marker within the robot's activity range, and the constraint relationship includes the posture relationship between the robot and the original map and / or other nodes, and / or the posture relationship between the characteristic marker and the original map and / or other nodes.

[0014] According to one aspect of the present invention, wherein the distance sensor is a laser radar, the step of constructing a map of the newly added area further comprises:

[0015] Control the robot to scan the surrounding environment;

[0016] An incremental map is constructed by extracting key frames from the scanning results and adding them to the sub-map.

[0017] According to one aspect of the present invention, the step of locating the position of the robot further comprises:

[0018] Control the distance sensor to scan the robot's surroundings;

[0019] The original map is converted into a point cloud, and the scanning results of the distance sensor are matched with the point cloud data of the original map to determine the position of the robot in the original map.

[0020] According to one aspect of the present invention, the original map is a png (a bitmap file storage format) format map or a raster map.

[0021] According to one aspect of the present invention, the mapping algorithm constructs an incremental map every 80 key frames.

[0022] According to one aspect of the present invention, the map construction method further includes:

[0023] Control the robot to return from the newly added area to the space corresponding to the original map;

[0024] Control the distance sensor to scan the robot's surrounding environment and determine the robot's current position;

[0025] Based on the current position of the robot, loop detection is performed on the newly added area map.

[0026] According to one aspect of the present invention, the step of performing loop closure detection on the newly added area map includes: controlling the robot to move within the spatial range corresponding to the original map, and repeating the step of performing loop closure detection on the newly added area map at the new current position.

[0027] According to one aspect of the present invention, the step of determining the corresponding position of the robot's current position in the original map also includes: establishing a nonlinear least squares equation and optimizing the regression coefficient through multiple iterations to determine the corresponding relationship between the robot's current position and the original map.

[0028] According to one aspect of the present invention, the map construction method further includes optimizing the newly added area map based on the constraint relationship between the node and the subgraph, and the corresponding relationship between the robot's current position in the original map.

[0029] According to one aspect of the present invention, the present invention further includes a robot, comprising:

[0030] main body;

[0031] a motion component, the motion component being disposed on the main body and being driven to move the robot;

[0032] a distance sensor fixedly mounted on the main body and configured to scan the surrounding environment of the robot and obtain the position of obstacles relative to the robot; and

[0033] A control system is provided on the main body and communicates with the motion component and the distance sensor. The control system is configured to execute the map building method as described above.

[0034] According to one aspect of the present invention, the present invention further includes a computer-readable storage medium, wherein the computer-readable storage medium includes computer-executable instructions stored thereon, and the executable instructions implement the control method as described above when executed by a processor.

[0035] Compared with the prior art, the embodiments of the present invention provide a map construction method for expanding map construction. By using the original map within the robot's range of activity, the robot directly constructs a map of the newly added area through the SLAM method while the robot is moving. The amount of data required for storage is greatly reduced, and there is no need to delete the original map. The original map and the markings of the newly added area can be completely retained, simplifying the operation process. In addition, loop detection can be performed at any position corresponding to the original map, thereby improving the flexibility and applicability of the map construction method. The embodiments of the present invention also include a robot and a computer-readable storage medium capable of running the aforementioned map construction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 is a flowchart of a map construction method in one embodiment of the present invention;

[0038] Figure 2 is a flowchart of a map construction method including a specific matching method of an original map in one embodiment of the present invention;

[0039] Figure 3 is a flowchart of a map construction method including a process of constructing an incremental map in one embodiment of the present invention;

[0040] Figure 4 is a flowchart of a map construction method including a process of determining a robot's position in an original map in one embodiment of the present invention;

[0041] Figure 5 is a flowchart of a map construction method including a loop closure detection process in one embodiment of the present invention;

[0042] Figure 6 is a flowchart of a map construction method including a newly added area map optimization process in one embodiment of the present invention;

[0043] Figure 7 This is a schematic diagram of the positional relationship between the original map corresponding area and the newly added area in one embodiment of the present invention;

[0044] Figure 8 1 is a structural block diagram of a robot in one embodiment of the present invention. DETAILED DESCRIPTION

[0045] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0046] In the description of the present invention, it should be understood that terms such as "center," "longitudinal," "transverse," "length," "width," "thickness," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," "clockwise," and "counterclockwise" are used to indicate positions or relationships based on those shown in the accompanying drawings. These terms are intended solely to facilitate description and simplify the description of the present invention and are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed to indicate or imply relative importance or to implicitly specify the number of the technical features referred to. Thus, features designated "first" or "second" may explicitly or implicitly include one or more of the designated features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0047] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed, removable, or integral connections; mechanical, electrical, or intercommunication connections; direct or indirect connections through an intermediary; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0048] In the present invention, unless otherwise expressly specified or limited, "above" or "below" a first feature may include direct contact between the first and second features, or may include contact between the first and second features not in direct contact but via another feature between them. Furthermore, "above," "above," and "above" a first feature may include both directly above and diagonally above the second feature, or simply indicate that the first feature is at a higher level than the second feature. "Below," "below," and "below" a first feature may include both directly above and diagonally above the second feature, or simply indicate that the first feature is at a lower level than the second feature.

[0049] The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numbers and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides examples of various specific processes and materials, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0051] Figure 1 The specific process of the map construction method 100 according to a preferred embodiment of the present invention is shown. Figure 7 The relationship between the original map area and the newly added area is shown below. Figure 7 Detailed description.

[0052] In this embodiment, the map construction method 100 is used to construct a map of the robot's activity range. The robot has a distance sensor, such as an infrared distance sensor, a sonar sensor, etc. The distance sensor is installed at a fixed position on the robot, has a fixed relative position relationship with the robot, and can obtain distance information of the surrounding environment. Combined with the installation angle of the distance sensor, point cloud information of the object can be obtained. In the SLAM method, while controlling the movement of the robot, the distance sensor is used to obtain information about the surrounding environment, and continuous environmental information is obtained. For example, the robot is controlled to move along a predetermined route at a speed of 0.5 meters per second. During the movement path, the distance sensor obtains information about the surrounding environment, such as continuous obstacles such as walls and pillars, space exits or closed boundaries, etc. Finally, the robot is controlled to return to its initial position to form a closed loop. By aligning the linear environmental information, the obstacle information and boundary information within the robot's activity area can be used to construct a map.

[0053] Expanded mapping refers to adding new activity areas outside the robot's original activity range. The newly added areas are mapped using the SLAM method, and the map of the newly added areas needs to be merged with the original map to form a new map of the robot's activity range. In existing methods, when constructing a map of the newly added area, a large amount of data needs to be stored, which puts a huge pressure on computing and storage. Alternatively, the original map can be directly deleted and the robot controlled to rebuild a map that includes the newly added area. When using the SLAM method to construct a map, dynamic obstacles within the robot's activity range should be eliminated as much as possible. Therefore, the method of reconstructing the map is not only cumbersome, but also difficult to clear dynamic obstacles for activity venues that have already been used in practice.

[0054] like Figure 1 As shown, this embodiment provides a map construction method for constructing a map of a newly added activity area of ​​a robot, such as Figure 7 As shown, a delivery robot used in a hotel has an original map. The robot is operating normally or has completed a closed-loop map. When a new area is added that the robot can enter, the map of the newly added area constructed by the robot using the SLAM method is independent of the original map. This embodiment provides a method for linking the newly added area map with the original map. This direct linking with the original map reduces data storage and simplifies the map construction process. In step S101, the robot is controlled to move to any point within the spatial range corresponding to the original map, and the robot's position is determined. First, the robot is located within the original map. The robot's specific position can be determined by detecting the environment using a distance sensor and comparing it with the original map features, or it can be manually input. The specific positioning method of the robot will be described in detail in subsequent embodiments. Specifically, the robot can be located anywhere within the spatial range corresponding to the original map, or it can be located in the original map adjacent to the newly added area. Preferably, a location with rich feature information in the original map, such as near a pillar with a unique shape, can be selected to improve the accuracy of the robot's positioning. The robot's current position can also be repositioned and optimized using an ICP (Iterative Closest Point) positioning algorithm to obtain a more accurate position relationship of the robot relative to the original map.

[0055] In step S102, the robot is controlled to move into the newly added area and scan the newly added area using the distance sensor. This embodiment uses the SLAM method to construct a map of the newly added area, and needs to use the distance sensor to obtain information about obstacles in the newly added area, such as the boundaries of the wall, the shape and position of fixed obstacles, the exit and entrance of the passage, etc. Figure 7As shown, the left side is the area corresponding to the original map, and the right side is the newly added area. In this step, the robot is controlled to move from the area corresponding to the original map to the newly added area, while continuously using the distance sensor to obtain information about the surrounding environment. For example, the distance sensor is a lidar, which completes 5000 ranging actions per second, and relies on the echo time or echo phase to obtain the distance information between the lidar and the echo point. Furthermore, it can be fitted into a plane or a curved surface to fully reflect the actual situation of the obstacle.

[0056] In step S103, the robot is controlled to move within the newly added area, continuously scanning the newly added area using the distance sensor, constructing a map of the newly added area, and determining the robot's position relative to the original map. Within the newly added area, the robot is controlled to move along a preset route at a fixed speed, for example, a constant speed of 0.5 meters per second. The movement path is pre-entered or automatically planned based on the robot's preset avoidance logic and obstacle information acquired by the distance sensor. Furthermore, due to the constraint relationship between the distance sensor and the robot, the distance information acquired by the distance sensor, combined with the angular relationship, can be used to obtain point cloud information of obstacles relative to the robot. This obstacle information is continuously mapped as the robot moves to construct a map of the newly added area. This step determines the robot's position relative to the original map, establishes a connection between the newly added area map and the original map, and ultimately generates a complete map containing the newly added area. Specifically, a SLAM algorithm can be used to calculate the robot's position relative to the original map while constructing the map of the newly added area, or the robot's motion device and other sensors can be used to calculate the distance and angular change of the robot from its initial position to determine the robot's position relative to the original map.

[0057] Figure 2 The specific steps of the map construction method 200 according to the preferred embodiment of the present invention are shown, which includes the process of coordinating with the original map. Figure 2 Detailed description.

[0058] In the map construction method 200, steps S201 and S202 are substantially the same as the map construction method S101 and S102, respectively, and are not further described here. In step S203, the original map is passed into the mapping algorithm as a sub-graph. This step only requires the original map information. Compared with the traditional method, which requires the storage of a large amount of information such as maps, laser data, nodes, etc., this step has less computational storage pressure. Furthermore, according to a preferred embodiment of the present invention, the original map is a png format map or a raster map, which ensures that the data format is smaller while ensuring that the map information is not missing. The mapping algorithm in this step can be executed by the robot's control system to construct a map of the newly added area, that is, to execute the SLAM method to establish a map of the newly added area. In other embodiments of the present invention, step S203 can be executed in advance and is not limited to after step S202. For example, the original map can be passed into the mapping algorithm as a sub-graph before positioning the robot or controlling the robot to enter the newly added area.

[0059] In step S204, the robot's scan data within the newly added area is added as an increment to the subgraph (original map). In step S203, the original map is added as a subgraph to the mapping algorithm. In this step, the mapping algorithm uses distance sensors to obtain information and builds a map of the newly added area based on the subgraph (original map). In step S205, the robot is controlled to move within the newly added area, continuously scanning the newly added area using the distance sensor, and recording the scan results for use in building a map of the newly added area in subsequent steps. Specifically, in step S206, the constraint relationships between nodes and subgraphs and / or other nodes are calculated in real time. According to a preferred embodiment of the present invention, the nodes in this step include the robot's current position and characteristic landmarks within the robot's range of motion. For example, after a preset time interval, the robot's current position within the newly added area is used as a node to calculate the constraint relationship between the robot's current position and the subgraph (original map). Characteristic landmarks within the robot's range of motion include locations where the point cloud data obtained by the distance sensor has unique changes or where the point cloud data changes are relatively intensive, which are reflected in the entity, such as characteristic locations such as walls with characteristic shapes or the ends of passages. Specifically, the constraint relationship in this step includes the posture relationship between the robot and the original map, the posture relationship between the feature marker and the original map, and the position relationship between the robot or feature marker and other nodes, such as the position relationship of the robot or feature marker in the previous key frame or the previous incremental map in the SLAM algorithm, or for example, the current position of the robot is used as a node, its position relative to the original map, or its relative position relationship relative to other nodes, such as the previous node, including angles and directions, or coordinate increments after coordinate conversion. The same applies to feature markers.

[0060] Figure 3The specific steps of the map construction method 300 according to the preferred embodiment of the present invention are shown, which includes the process of constructing an incremental map. Figure 3 Detailed description.

[0061] like Figure 3 As shown, steps S301, S302, S303, S304, S305, and S306 in map construction method 300 are substantially identical to steps S201, S202, S203, S204, S205, and S206 in map construction method 200, respectively, and are omitted here. The distance sensor in this embodiment is a lidar, which transmits and receives laser light, collects the laser flight time or phase change, and calculates the distance between the lidar and the location of the reflected echo.

[0062] In step S307, the robot is controlled to scan its surroundings to obtain information about the distance and angle between the obstacle and the robot. In step S308, keyframes are extracted from the range sensor scans to construct an incremental map. For example, in this embodiment, the lidar includes 200 laser channels and can perform ranging operations 10,000 times per second. Each ranging operation can obtain point cloud information for up to 200 echo positions, forming a frame of obstacle image. In this embodiment, keyframes are extracted from the lidar scan results to construct an incremental map. In a preferred embodiment of the present invention, an incremental map is constructed every 80 keyframes, and the incremental map is added to a submap. Multiple incremental maps are superimposed to form a complete map. According to a preferred embodiment of the present invention, keyframes can be extracted according to a preset method, such as at a preset number of intervals or a preset time interval, and selected as keyframes. In this embodiment, the original map is stored as a submap in the mapping algorithm. When the robot moves in the newly added area and scans using the range sensor, the obtained keyframes are used to construct an incremental map according to the aforementioned method. The keyframes are added to the submap (original map) and extended to ultimately construct a map that includes the newly added area.

[0063] Figure 4 The specific steps of the map construction method 400 according to the preferred embodiment of the present invention are shown, which includes the positioning process of the robot in the corresponding area of ​​the original map. Figure 4 In the map construction method 400 of this embodiment, steps S403, S404, S405, S406, S407, S408 and S409 are substantially the same as steps S302, S303, S304, S305, S306, S307 and S308 in the map construction method 300, and are not described in detail here.

[0064] As in the aforementioned embodiment, the robot needs to be positioned before entering the newly added area. In step S401, the distance sensor is controlled to scan the periphery of the robot to obtain information about surrounding obstacles at the robot's location. For example, in a preferred embodiment of the present invention, the distance sensor is a laser radar, and it faces a certain direction or circumferentially to obtain point cloud information of obstacles around the robot. In step S402, the original map is converted into a point cloud, and the scanning result of the distance sensor is matched with the point cloud data of the original map to determine the position of the robot in the original map. In this embodiment, the point cloud data of the original map is matched and compared with the point cloud data of the surrounding obstacles obtained by the robot at the current position to determine the position of the robot in the original map. Furthermore, the initial position of the robot in the original map can be set at a location with a characteristic structure, such as a special structure of a pillar or wall, to facilitate the distinction and comparison of point cloud data. If the point cloud data obtained by the robot at the current position is compared and matched with the point cloud data of the original map, no corresponding position is obtained, or there are multiple matching point cloud data in the original map, for example, in a corridor with similar wall structures on both sides, there may be multiple positions with similar point cloud data in the original map. When the above situation occurs, according to a preferred embodiment of the present invention, the robot movement can be controlled to change the current position of the robot, and steps S401 and S402 are re-executed until the position of the robot in the original map is determined. If the position of the robot cannot be determined after multiple matches, it is considered that there is an error in the original map or there is a large error in the scanning result of the distance sensor, and the map construction process of the new area cannot be carried out. The original map and distance sensor of the robot should be tested.

[0065] Figure 5 The steps of a map construction method 500 according to a preferred embodiment of the present invention are shown, which specifically includes the process of loop detection of the newly added area map. Figure 5 Detailed description. Steps S501, S502, S503, S504, S505, S506, S507, S508, and S509 in map construction method 500 are substantially the same as steps S401, S402, S403, S404, S405, S406, S407, S408, and S409 in map construction method 400, respectively. In this embodiment, after constructing a map of the newly added area, the map of the newly added area is optimized and tested using subsequent methods.

[0066] In step S510, the robot is controlled to return from the newly added area to the spatial range corresponding to the original map. Specifically, in this embodiment, the robot's return position can be the same as the robot's initial position in step S501, or it can be different from the robot's initial position. For example, if the newly added area is a passage with different entrances and exits, the robot can be controlled to return to a different position in the original map. This embodiment does not limit the robot's closed-loop position, greatly improving its flexibility and applicability compared to traditional SLAM map construction methods.

[0067] In step S511, the range sensor is controlled to scan the robot's surroundings and determine the robot's current position. Specifically, the method for determining the robot's current position can be the same as determining the robot's position on the original map in step S502. Point cloud data of the surrounding environment is obtained through the range sensor and matched with the point cloud data of the original map. In this embodiment, the position the robot returns to in the original map is not limited to the initial position. Therefore, a position that improves positioning accuracy can be selected, such as a location with a characteristic point cloud data.

[0068] In step S512, based on the robot's current position, a loop closure check is performed on the map of the newly added area. A closed loop is established by combining the robot's initial and return positions in the original map. Correction and compensation are performed on the point cloud information acquired during the robot's movement within the newly added area, resulting in a map of the newly added area that is linked to the original map. When constructing a map using the SLAM method, there is a certain degree of error in determining the robot's current pose. The robot's pose estimation is a recursive process based on the previous frame. Therefore, the error in the robot's pose estimation gradually accumulates. This step uses a loop closure check method to not only close the loop using the estimated data from the previous frame or previous position (node), but also uses a branch-and-bound method to select multiple frames or multiple positions for loop closure, thereby optimizing the robot's pose and obtaining a more accurate map of the newly added area. Furthermore, frames farther away can be selected for loop closure check to avoid excessive similarity between adjacent key frames. In some embodiments of the present invention, a key frame is selected every 80 seconds, which can also serve as a reference frame for loop closure check.

[0069] Further, such as Figure 6As shown, steps S601, S602, S603, S604, S605, S606, S607, S608, S609, S610, S611 and S612 in the map construction method 600 are basically the same as steps S501, S502, S503, S504, S505, S506, S507, S508, S509, S510, S511 and S512 in the map construction method 500, respectively. In step S613, the robot is controlled to move within the spatial range corresponding to the original map, and loop detection is performed on the newly added area at the new current position. The loop detection step is repeated to improve the accuracy of the map of the newly added area. Furthermore, in step S614, the map of the newly added area is optimized based on the constraint relationship between the node and the subgraph and the corresponding relationship between the robot's current position in the original map. In step S607, the constraint relationship between the node and the subgraph is calculated in real time. According to the aforementioned embodiment, the node can be used as the node for error allocation in the newly added area map. The robot's motion and distance sensors are subject to error. Accurately locating the position of the robot and other nodes requires providing an unbiased map. However, constructing an unbiased map using SLAM methods requires accurate positioning. Therefore, to obtain a more accurate map of the newly added area, a multiple-iteration method can be used. According to a preferred embodiment of the present invention, a nonlinear least squares equation is established and the regression coefficient is optimized through multiple iterations to determine the corresponding relationship between the robot's current position and the original map, thereby improving the accuracy of the robot's positioning and further improving the accuracy of the newly added area map. For example, a Gauss-Newton algorithm is used for precise matching to obtain the robot's precise position. The node at this position forms a constraint with the original map. Then, the constraints between the node and the subgraph established during the least squares optimization process are used to obtain the optimized map of the newly added area.

[0070] Figure 8 The following is a block diagram of a robot 1 according to a preferred embodiment of the present invention. The robot 1 comprises a main body, a motion assembly 10, a distance sensor 20, and a control system 30. The main body is the primary structural framework of the robot 1, and all components of the robot 1 are secured to the main body. The main body can be made of an alloy or organic material to provide a fixed structure. The various components of the robot 1 are mounted in corresponding positions within the main body. The motion assembly 10 is mounted on the main body and can be driven to move the robot 1. Preferably, the motion assembly 10 utilizes a wheeled structure, such as a universal wheel driven by an electric motor, to ensure that the robot 1 remains parallel to the ground during movement, thereby preventing significant height and angle changes in the distance sensor 20 mounted on the main body during movement.

[0071] The distance sensor 20 is fixedly mounted on the main body and has a fixed constraint relationship with the main body. It can scan the surrounding environment of the robot 1 and obtain the position of obstacles relative to the robot 1. Preferably, there are multiple distance sensors 20 installed on the robot 1 to comprehensively obtain environmental information around the robot 1. The distance sensor 20 can be a laser sensor, sonar sensor, etc. According to a preferred embodiment of the present invention, the distance sensor 20 is a laser radar. The control system 30 is mounted on the main body and communicates with the motion component 10 and the distance sensor 20. For example, the control system 30 is a processor that communicates with the motion component 10 and the distance sensor 20 via a data line or wireless communication. It can control the operation of the motion component 10 and the distance sensor 20 and receive the surrounding environment information obtained by the distance sensor 20. The control system 30 in this embodiment can execute the map construction method in the aforementioned embodiment to construct a map of the motion range of the robot 1.

[0072] According to a preferred embodiment of the present invention, a computer-readable storage medium is further included, including computer-executable instructions stored thereon, and the executable instructions implement the control method as described above when executed by a processor.

[0073] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A map construction method for constructing a map within the range of a robot's activity, wherein the robot has a range sensor and an original map within the range of the robot's activity, the map construction method comprising: Control the robot to move to the space corresponding to the original map and locate the robot's position; Control the robot to move to the newly added area and use the distance sensor to scan the newly added area; Control the robot to move within the newly added area, continuously scan the newly added area using the range sensor, build a map of the newly added area, and determine the robot's position relative to the original map; Control the robot to return from the newly added area to the space corresponding to the original map; Control the distance sensor to scan the robot's surrounding environment and determine the robot's current position; Based on the current position of the robot, loop detection is performed on the newly added area map.

2. The map construction method according to claim 1, wherein the step of constructing a map of a newly added area further comprises: Pass the original map as a subgraph into the mapping algorithm; Adding the robot's scanning data in the newly added area as an increment to the subgraph; The constraint relationship between the node and the subgraph and / or other nodes is calculated in real time, where the node includes the current position of the robot and / or the characteristic marker within the robot's activity range, and the constraint relationship includes the posture relationship between the robot and the original map and / or other nodes, and / or the posture relationship between the characteristic marker and the original map and / or other nodes.

3. The map construction method according to claim 2, wherein the distance sensor is a laser radar, and the step of constructing a map of the newly added area further comprises: Control the robot to scan the surrounding environment; An incremental map is constructed by extracting key frames from the scanning results and adding them to the sub-map.

4. The map construction method according to claim 1, wherein the step of locating the position of the robot further comprises: Control the distance sensor to scan the robot's surroundings; The original map is converted into a point cloud, and the scanning results of the distance sensor are matched with the point cloud data of the original map to determine the position of the robot in the original map. The map construction method according to claim 1 , wherein the original map is a PNG format map or a raster map.

6. The map construction method according to claim 3, wherein the mapping algorithm constructs an incremental map every 80 key frames.

7. The map construction method according to claim 1, wherein the step of performing loop detection on the newly added area map comprises: Control the robot to move within the spatial range corresponding to the original map, and repeat the steps of loop detection on the newly added area map at the new current position.

8. The map construction method according to claim 1 or 7, wherein the step of determining the corresponding position of the robot's current position in the original map further comprises: A nonlinear least squares equation is established, and the regression coefficient is optimized through multiple iterations to determine the correspondence between the robot's current position and the original map.

9. The map construction method according to claim 8 further includes optimizing the newly added area map based on the constraint relationship between the node and the subgraph, and the corresponding relationship between the robot's current position in the original map.

10. The map construction method according to claim 1, wherein the step of determining the position of the robot relative to the original map comprises: The robot's motion device and sensors are used to calculate the distance and angle changes of the robot from its initial position, and to determine the robot's position relative to the original map.

11. A robot comprising: main body; a motion component, the motion component being disposed on the main body and being driven to move the robot; a distance sensor, the distance sensor being fixedly mounted on the main body and configured to scan the surrounding environment of the robot and obtain the position of obstacles relative to the robot; and A control system is provided on the main body and communicates with the motion component and the distance sensor, wherein the control system is configured to execute the map construction method according to any one of claims 1 to 10.

12. A computer-readable storage medium comprising computer-executable commands stored thereon, wherein the computer-executable commands, when executed by a processor, implement the map construction method according to any one of claims 1 to 10.

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