Remote manual coordinate repositioning method for indoor robot

Abstract

The invention relates to the technical field of robots, and discloses an indoor robot remote manual coordinate repositioning method, which comprises the following steps that: step 1, a positioning loss state is identified, and a platform makes an action: when a robot cannot maintain reliable positioning, an alarm is sent to the platform in time, and an operator starts a repositioning process after receiving and confirming the alarm; 2, constructing a new map: setting a new map construction mechanism; step 3, calculating a target pose: manually aligning the new map and the old map by an operator to obtain the target pose; and 4, repositioning is carried out, wherein internal updating is achieved through the robot according to the target pose. According to the method, intelligent guidance of an automatic algorithm and flexible decision of manual operation are fused, the defects that pure automatic repositioning is prone to failure and falling into local optimum in a large-range loss or complex environment are effectively overcome, and meanwhile the problems that traditional manual intervention needs field operation and is low in efficiency are avoided.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a method for remotely and manually relocating coordinates of an indoor robot. Background Art

[0002] Mobile robots are robotic systems that can move and operate autonomously or semi-autonomously in the environment. They are usually equipped with sensors, control systems, drive systems and communication modules, and can perform various tasks without direct human intervention.

[0003] In the mobile robot positioning system based on laser SLAM technology, although it has demonstrated efficient application value in the field of indoor navigation, it still faces many challenges when facing complex and changeable actual environments. For example, in a dynamic interference environment, such as frequently moving obstacles blocking the laser feature points originally used for positioning, or in areas with sparse features such as long corridors, the lack of sufficient environmental information for self-positioning updates will lead to a decrease in positioning accuracy or even positioning loss. In addition, sudden hardware failures, such as packet loss during radar data transmission, will also seriously affect the normal positioning function of the robot. Once encountering the above situation, the robot may not be able to accurately judge its own position, thereby triggering the so-called "kidnapping detection" state. At this time, the robot loses the accurate and reliable pose estimation on the map. Therefore, it is necessary to study the robot's relocalization technology when facing the above problems. Summary of the Invention

[0004] The purpose of the present invention is to remotely obtain robot map information through the network after the robot loses its positioning, manually match the map, and realize remote manual repositioning of the robot.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for remotely and manually relocating coordinates of an indoor robot comprises the following steps: 1. identifying a positioning loss state and reporting it to a platform; 2. initiating a relocalization process after an operator receives and confirms an alarm; 3. the robot starts building a new map; 4. the platform recommends an optimal scanning area; 5. the operator moves the robot based on the recommendation and improves the new map data; 6. the robot reports the new and old map data; 7. the platform visualizes the new and old maps; 8. the operator manually aligns the new and old maps; 9. calculating a target pose; 10. the platform issues a pose correction instruction; and 11. the robot is forced to update its pose and completes relocalization.

[0007] As a preferred technical solution of the present invention, in step 1, the positioning loss state includes a sharp drop in positioning confidence, unreasonable jumps in pose estimation, and failure of SLAM algorithm reporting; when the loss state occurs, the robot immediately stops autonomous movement, maintains the lidar scanning and communication functions, and sends a positioning loss event message to the central management platform.

[0008] As a preferred technical solution of the present invention, step 3 specifically includes the following steps: reset: clear or pause the data related to the old map; initialize the new map: start a new SLAM instance with the current physical position of the robot as the origin of the new coordinate system; generate new data: start building a new map, the laser radar and encoder data in the robot continue to be collected, and its own position in the new map coordinate system is recorded in real time and transmitted to the platform.

[0009] As a preferred technical solution of the present invention, step 4 includes data acquisition: collecting old map data; feature extraction: using a combination of line segment extraction and intersection methods to perform image processing, perform edge detection on the old map, extract straight line segments from the edges, and calculate line segment intersections; area screening and sorting: clustering the extracted high-feature points to form several high-feature areas; visual recommendation: marking on the platform map interface, and forming a recommended path based on the robot's last reported posture.

[0010] As a preferred technical solution of the present invention, in step 5, if the robot fails to successfully reach the recommended high-feature area, a progressive spiral search mechanism is set to expand the search range.

[0011] As a preferred technical solution of the present invention, in step 5, the progressive spiral search mechanism includes: setting the spiral line density and angle increment, and the spiral line density is less than the robot's laser radar recognition distance, taking the current physical position as the starting point, generating an Archimedean spiral, and the operator controls the robot to move and scan step by step along the route until it enters one of the high-feature areas.

[0012] As a preferred technical solution of the present invention, in step 7, the old map and the new map are loaded simultaneously in the map display engine, and the two maps are rendered using different colors and line types to distinguish them.

[0013] As a preferred technical solution of the present invention, in step 8, the position of the old map layer is kept unchanged, the operator uses the tool to drag the new map layer for translation, and uses the rotation control to rotate the new map layer until the corresponding parts in the map completely overlap.

[0014] As a preferred technical solution of the present invention, in step 8: when the new and old maps only partially overlap and the rest are severely misaligned or completely mismatched, the operator directs the robot to move from the currently aligned position in the direction of the misalignment, perform a small-scale supplementary scan, and then return to step 6; when there is ambiguity in the alignment of the new and old maps, directly return to step 4 and increase the threshold of the feature extraction algorithm.

[0015] As a preferred technical solution of the present invention, in step 9, the platform system records in real time the total geometric transformation performed by the operator on the new map, records the translation vector and rotation angle of the new map, and obtains the target pose.

[0016] The beneficial effects of the present invention are:

[0017] Compared with the existing technology, the remote manual relocalization method for indoor robots proposed in the present invention cleverly combines the intelligent guidance of automatic algorithms with the flexible decision-making of manual operations, effectively overcoming the defects of pure automatic relocalization that are prone to failure and falling into local optimality when a large area is lost or in a complex environment, while avoiding the problem of traditional manual intervention requiring on-site operation and low efficiency. The platform intelligently recommends the best scanning area and provides visual navigation, which greatly reduces the operator's judgment difficulty and operation threshold. The introduction of progressive spiral search as a backup strategy ensures that the environment exploration can still be systematically completed when the recommended path fails. A closed-loop processing mechanism based on alignment result feedback is designed, which can improve the alignment success rate by targeted methods such as supplementary scanning or increasing feature thresholds. The entire solution achieves remote and efficient intervention while ensuring high reliability and strong robustness, significantly improving the autonomy and operation efficiency of the robot system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of a method for remote manual relocation of coordinates of an indoor robot proposed by the present invention;

[0019] Figure 2 This is a flow chart of a method for remote manual relocation of coordinates of an indoor robot proposed by the present invention;

[0020] Figure 3 A flowchart for setting the recommended scan path;

[0021] Figure 4 A system diagram of the progressive spiral search mechanism;

[0022] Figure 5 The new map uploaded by the robot and the old map are displayed on the platform;

[0023] Figure 6 This is the display image after the new map is aligned with the old map;

[0024] Figure 7 This is a physical picture of the robot of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0026] Before introducing the technical solutions described in this specification, a brief introduction to the indoor robot repositioning method in the prior art is given;

[0027] When a robot triggers the "kidnapping detection" state, traditional solutions are generally divided into two types:

[0028] 1. Automatic relocalization algorithm: This type of method aims to allow the robot system to recover from a lost position state without relying on human intervention. The core idea is to re-estimate the robot's position by matching the current sensor data with the known map. It generally includes:

[0029] Probabilistic relocalization method based on particle filtering: The environment is modeled as a series of possible positions, called particles. Each particle is assigned a weight based on the degree of match between the current laser scan data and the map. Through iterative updates, particles with high weights gradually converge to the true pose. This method is suitable for relocalization of completely unknown initial positions. However, in symmetrical or repetitive structured environments, such as multiple identical rooms and long corridors, the matching scores of multiple positions are similar, resulting in "particle degradation", that is, the weights of a large number of particles converge and cannot be effectively converged.

[0030] Scan matching algorithms such as the ICP algorithm and its variants: By iterating the closest point or its improved versions such as NDT, the current laser scan data is aligned with the scan data in the map, and the optimal transformation matrix is ​​solved to restore the pose. This method has high accuracy when the features are rich and the initial pose is close to the true value. However, this method is highly sensitive to the initial pose. If the robot completely loses its positioning, that is, the initial guess pose deviation is large, it is easy to fall into the local optimum and the matching fails. Especially in large-scale maps, the matching process is time-consuming and performs poorly in low-feature areas.

[0031] Multi-sensor fusion solution: This solution combines information from multiple sensors, such as lidar, IMU (inertial measurement unit), wheel encoders, and visual cameras, to improve the redundancy and robustness of the positioning system. In the event of a single sensor failure, such as when the radar is blocked, other sensors can provide auxiliary information, effectively suppressing noise and improving stability in dynamic environments. However, this solution significantly increases hardware costs and requires the purchase and integration of multiple sensors. Furthermore, the system is highly complex, requiring the design of complex fusion algorithms, such as EKF, UKF, and graph optimization, to coordinate data from different sensors.

[0032] 2. Manual intervention solution: The operator goes to the robot's location and triggers the reset or repositioning mode through physical buttons to achieve on-site manual repositioning, but the operation is time-sensitive.

[0033] In summary, the automatic relocation algorithm uses a program to automatically calculate relocation, which needs to be calculated within a limited search space. If the search range is incorrect, positioning cannot be performed, and a similar but incorrect location is located. The calculation takes too long and times out, which means there is a possibility of positioning failure. The manual intervention solution requires personnel to arrive at the site, which is time-consuming and labor-intensive. Frequent intervention affects operational efficiency.

[0034] In addition, before introducing the solution of the present invention, a brief introduction to the robot system is given. The robot system is composed of a terminal robot and a central management platform. The behaviors of the terminal robot include: the lidar continuously scans the surrounding environment to obtain obstacle point cloud data; the code disk or encoder on the action wheel records the robot's wheel speed and mileage information in real time; the robot's internal SLAM (Simultaneous Localization and Mapping) algorithm integrates the lidar data and the code disk data to perform real-time self-positioning and determine its own position on the constructed global map, specifically the position coordinates x, y and the orientation angle θ. The platform behavior is that the platform continuously receives and displays the robot's real-time position, status and map information.

[0035] Based on the above description, please refer to the attached Figure 1-Figure 7 The present invention proposes a method for remote manual repositioning of an indoor robot, comprising the following steps:

[0036] Step 1. Identify the positioning loss state and report to the platform: When the robot cannot maintain reliable positioning, it will promptly send an alarm to the platform and start the relocalization process. Specifically, when the robot cannot maintain reliable positioning due to reasons such as dynamic obstacle occlusion, feature sparse areas or temporary sensor failure, it is determined to be positioning lost. The robot's internal positioning monitoring module detects a sharp drop in positioning confidence, unreasonable jumps in pose estimation, or failure of the SLAM algorithm report. At this time, it is confirmed to enter the "kidnapping detection" state. To avoid collision, the robot immediately stops autonomous movement, but maintains lidar scanning and communication functions, and sends a positioning loss event message to the central management platform through communication modules such as Wi-Fi, 4G, and 5G. The message contains the robot ID, timestamp, and the last reported low-confidence pose.

[0037] Step 2: Platform action: After the operator receives and confirms the alarm, the relocalization process is officially started, instructing the robot to enter a specific mode. Specifically, after the platform receives the positioning loss information, it prominently prompts the operator on the interface that a robot has lost its positioning, for example, by highlighting the robot on the user interface and popping up a notification requesting manual intervention. After the operator confirms the alarm, the relocalization process is started. The platform generates and sends a control message containing the "enter_manual_relocalization_mode" command to the target robot. The robot receives and interprets the command and confirms that it has entered the manual relocalization mode.

[0038] Step 3: The robot starts building a new map: It re-establishes an independent local coordinate system at its current location to provide a basis for subsequent alignment, including:

[0039] Reset: After receiving the command, the robot clears or pauses the SLAM calculation status and map data related to the old map;

[0040] Initialize a new map: Use the robot's current physical position as the origin of the new coordinate system (0, 0, 0) and start a new SLAM instance. This can be the same algorithm as the main SLAM, such as the Cartographer algorithm.

[0041] Generate new data: Start building an independent local map, called a new map. LiDAR and encoder data continue to be collected, and the position (x_new, y_new, θ_new) of the object in the new map coordinate system is recorded in real time and transmitted to the platform.

[0042] Step 4: Improve the new map based on the platform’s calculation and recommendation of the best scanning area: Use the algorithm to intelligently analyze the old map, combine it with the robot’s position, find the scanning target area that is most conducive to fast and accurate repositioning, and guide the operation. Please refer to the attached Figure 3 ,include:

[0043] Obtaining data: The platform already has the old map, which can be used in the Occupancy Grid Map format, as well as the approximate position (x_last, y_last) last reported by the robot;

[0044] Feature extraction: Using a combination of line segment extraction and intersection or endpoint methods, OpenCV or PCL (PointCloud Library) is used for image or mesh processing. Edge detection or direct contour extraction is performed on the binary raster image of the old map. Line segment detection algorithms such as the Probabilistic Hough Transform are used to extract straight line segments from the edges. The intersections and endpoints of all line segments are calculated. These points usually correspond to strong features such as wall corners, door frames, and pillars.

[0045] Optionally, calculate the curvature or corner response of the local area, such as a variant of Harris Corner Detector, to supplement the recognition of non-linear features;

[0046] Region screening and sorting: Cluster the extracted high-feature points to form several high-feature regions. Specifically:

[0047] Set the current search range, for example, 10m in length and 5m in width, set the increment, for example, each search increases the length by 2m and the width by 1m, and set the feature threshold, for example, > 2 feature points in the area;

[0048] With (x_last, y_last) as the search center, search within the set current search range to filter out high-feature areas within the area. The filtered areas are weighted and scored based on feature strength (density of feature points within the area) and distance (distance to (x_last, y_last)). Areas with close distance and strong features have the highest priority. If there are no high-feature areas within the current search range, the range is expanded by the set increment.

[0049] Visual recommendation: On the platform map interface, the best recommended area is marked with a striking graphic, and a navigation arrow points from the robot's current position to the recommended area.

[0050] Step 5. The operator performs remote control based on the recommendation: The operator uses the intelligent recommendation provided by the platform to command the robot to move to the optimal scanning position, receive and interpret the remote movement instructions from the platform, execute the instructions, and drive the action wheels to move to the target area. In addition, during the movement process, the lidar continues to scan, the encoder continues to record mileage, and the new SLAM instance continues to work, integrating the newly collected data, continuously expanding and updating the new map to ensure that it contains rich features of the recommended area. During the movement, the operator needs to closely observe the new map and sensor data to ensure that the robot is moving in the right direction and adjust the direction and path according to the actual situation. For example, when encountering an obstacle, the operator controls the robot to bypass the obstacle, or when the operator identifies obvious and sufficiently rich features based on the map data uploaded in real time, the search is paused and the next step is entered;

[0051] It is worth noting that if the robot fails to successfully reach the recommended high-feature area, for example, encounters an obstacle or finds that the area has been changed, the operator expands the search range.

[0052] For further details, please refer to the attached Figure 4, set up a progressive spiral search mechanism, set the spiral line density b and angle increment Δθ. If the robot's lidar recognition distance is r, then b is less than r, ensuring that adjacent scanning circles have sufficient overlap to avoid missing feature areas. Take the current position (x_current, y_current) as the starting point, use the formula r = bθ to generate an Archimedean spiral, and control the robot to move and scan step by step along this route until it enters one of the high-feature areas or the operator determines that there are enough features in the map.

[0053] Step 6. The robot reports the old and new map data: The key data used for alignment is uploaded to the platform to provide material for subsequent manual alignment. Specifically, when the operator believes that the new map is sufficient or the robot reaches the recommended area and completes the scan, the map data is uploaded to the platform. The robot uploads the old map (i.e., the global map file before positioning loss) and the current complete new map (i.e., the local map file constructed in manual relocalization mode) to the server via the network. Optionally, the robot's current pose in the new map coordinate system is also uploaded as a reference, which is received and stored by the platform server.

[0054] Step 7: Platform visualization of old and new maps: Provide operators with a clear visual comparison environment to facilitate map alignment. Specifically, in the map display engine, load the old map and the new map at the same time, and use different colors and line types to render the two maps to distinguish them. Please refer to the attached Figure 5 ,In the figure, the old map boundary is shown in white, and the new map boundary is shown in black;

[0055] Step 8. Operator manually aligns the new and old maps: Through manual intervention, the two maps are precisely matched and the spatial transformation relationship between them is determined. Specifically, the operator uses a mouse or touch screen to drag the new map layer to translate, and uses the rotation control to rotate the new map layer. Carefully adjust so that the geometric features such as walls, corners, and pillars in the new map completely overlap or align with the corresponding parts in the old map to the greatest extent. Please refer to the attached Figure 6 , the operator moves and rotates the black boundary so that the black boundary coincides with the white boundary. At this time, the new map is completely aligned with the old map;

[0056] It is worth noting that when the new and old maps only partially overlap and the rest are seriously misaligned or completely mismatched, for example, a corner, a corridor, or a pillar in the new map can perfectly overlap with the old map, but other parts of the new map cannot be found on the old map, or the direction and distance are completely wrong, the main reason is that the new map is incomplete or drifting: during the movement of the robot, although the SLAM algorithm captures local features, due to the long movement distance, sparse environmental features, or slight slippage, the odometry accumulates errors, causing the overall structure of the new map to be distorted or translated. At this time, the operator instructs the robot to start from the current (aligned) position and move in the misaligned direction to perform a small-scale, high-precision supplementary scan, and then re-enter step 6 and operate sequentially;

[0057] When there are multiple solutions or ambiguities in the alignment of the new and old maps, for example, the new map shows a T-junction, while the old map has three identical T-junctions. It is impossible to determine which one is the correct one. In other words, there are many areas with similar geometric structures in the environment, and the collected feature points are not unique in the global map. In this case, directly return to step 4 and increase the threshold of the feature extraction algorithm: for example, change feature points > 2 to feature points > 5, or require that more unique features such as non-right angles and special shapes must be included.

[0058] Step 9. Calculate the offset and target pose: Based on the results of the manual alignment, calculate the precise position and orientation of the robot in the old map coordinate system. Specifically, the platform system records the total geometric transformation performed by the operator on the new map in real time:

[0059] Translation vector (Δx, Δy): the distance moved from the initial upload position of the new map to the final alignment position;

[0060] Rotation angle (Δθ): The amount of rotation from the initial orientation to the final alignment orientation, where the angle must be normalized.

[0061] Calculate the target pose: Since the robot starts scanning at the origin (0, 0, 0) of the new map, its target pose (x_target, y_target, θ_target) in the old map coordinate system is equal to the recorded transformation:

[0062] x_target = Δx;

[0063] y_target = Δy;

[0064] θ_target = Δθ;

[0065] Δx, Δy, Δθ are the transformations required to transform the new map from its initial uploaded position to a position aligned with the old map.

[0066] Step 10. The platform issues a pose correction command: Send the calculated precise pose to the robot, forcing it to update its internal state. The calculated (x_target, y_target, θ_target) is packaged into a forced pose correction command, such as in JSON format: {"cmd": "force_pose", "x": x_target, "y": y_target, "theta": θ_target});

[0067] The command is sent to the target robot through a secure communication channel such as TLS-encrypted MQTT. After receiving the command, the robot enters a standby state and prepares to update its posture.

[0068] Step 11. The robot is forced to update its posture and complete relocalization: the robot accepts the new global posture, reintegrates into the original map, and resumes normal function. Specifically, after receiving the forced posture instruction, the robot immediately stops all current positioning-related calculations, and forces its internal posture estimate to be set to (x_target, y_target, θ_target), notifying the SLAM system that the posture has been reset, the robot has successfully relocated in the old map coordinate system, exits the manual relocalization mode, and resumes normal SLAM tracking and autonomous navigation capabilities. The platform updates the robot position on the interface, indicating that the relocalization is successful, and can record the operation log.

[0069] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for remote manual relocation of coordinates of an indoor robot, characterized in that: The following steps are involved: Step 1: Identify the position loss state and the platform initiates an action: When the robot cannot maintain reliable positioning, it promptly sends an alarm to the platform. After the operator receives and confirms the alarm, the repositioning process is initiated; Step 2. Build a new map: Set up a new map construction mechanism, using the current robot's physical location as the origin and combining the robot's posture to create a new map with high-feature areas according to the construction mechanism. Step 3: Calculate the target pose: The operator manually aligns the new map with the old map to obtain the target pose; Step 4: Repositioning: The robot performs internal updates based on the target posture to achieve the repositioning purpose.

2. The method for remote manual relocation of coordinates of an indoor robot according to claim 1, characterized in that: In step 1, the positioning loss state includes a sharp drop in positioning confidence, unreasonable jumps in pose estimation, and failure of the SLAM algorithm report; When a lost state occurs, the robot immediately stops autonomous movement, maintains lidar scanning and communication functions, and sends a positioning loss event message to the central management platform.

3. The method for remote manual relocation of coordinates of an indoor robot according to claim 2, characterized in that: In step 2, the new map construction mechanism includes: Build preparation: Start building a new SLAM instance; Set recommended scanning path: Analyze old maps, divide high-feature areas, and combine the robot's last reported position to form a recommended path; Perfecting the new map: The operator teleoperates the robot based on the recommended scan path.

4. The method for remote manual relocation of coordinates of an indoor robot according to claim 3, characterized in that: The construction preparation in step 2 specifically includes: Reset: Clear or pause the data related to the old map; Initialize a new map: start a new SLAM instance with the robot's current physical position as the origin of the new coordinate system; Generate new data: Start building a new map. The robot's lidar and encoder data continue to be collected, and its position in the new map coordinate system is recorded in real time and transmitted to the platform.

5. The method for remote manual relocation of coordinates of an indoor robot according to claim 4, characterized in that: The setting of the recommended scanning path in step 2 specifically includes: Get data: collect old map data; Feature extraction: Using a combination of line segment extraction and intersection point methods to perform image processing, perform edge detection on the old map, extract straight line segments from the edges, and calculate line segment intersection points; Region screening and sorting: cluster the extracted high-feature points to form several high-feature regions; Visual recommendation: Mark on the platform map interface and form a recommended path based on the robot's last reported position.

6. The method for remote manual relocation of coordinates of an indoor robot according to claim 5, characterized in that: In the improved new map of step 2, when the robot is controlled to walk along the recommended path, the operator can change the direction and path of the robot according to the actual situation; If the robot fails to successfully reach the recommended high-feature area, a progressive spiral search mechanism is set up to expand the search range.

7. The method for remote manual relocation of coordinates of an indoor robot according to claim 6, characterized in that: The progressive spiral search mechanism includes: setting the spiral line density and angle increment, and the spiral line density is smaller than the robot's laser radar recognition distance, using the current physical position as the starting point, generating an Archimedean spiral, and the operator controls the robot to move and scan step by step along the route until it enters one of the high-feature areas.

8. The method for remote manual relocation of coordinates of an indoor robot according to claim 7, characterized in that: The step 3 comprises: The robot reports the old and new map data: uploading key data for alignment to the platform to provide material for subsequent manual alignment; The platform visualizes the old and new maps: in the map display engine, the old and new maps are loaded at the same time, and the two maps are rendered with different colors and line types to distinguish them; The operator manually aligns the old and new maps: the old map layer remains in place, and the operator uses the tool to drag the new map layer to translate it, and uses the rotation control to rotate the new map layer until the corresponding parts of the map completely overlap; Calculate offset and target pose: Based on the results of manual alignment, calculate the robot's precise position and orientation in the old map coordinate system.

9. The method for remote manual relocation of coordinates of an indoor robot according to claim 8, characterized in that: In step 3 of the manual alignment of the old and new maps by the operator: When the new and old maps only partially overlap and the rest are severely misaligned or completely mismatched, the operator directs the robot to start from the current aligned position and move in the misaligned direction, perform a small-scale supplementary scan, and then return to the step where the robot reports the new and old map data.

10. The method for remote manual relocation of coordinates of an indoor robot according to claim 9, characterized in that: In the manual alignment of the new and old maps by the operator in step 3, if there is ambiguity in the alignment of the new and old maps, the operator directly returns to step 2 and sets the high-feature area again.

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