Positioning method based on reflective column, storage medium and electronic equipment

Abstract

The invention discloses a positioning method based on a reflective column, a storage medium and electronic equipment. The positioning method comprises the following steps: obtaining global coordinates of the reflective column; identifying and extracting a reflective column in a radar range, and obtaining coordinates of the reflective column in a radar coordinate system; based on TF conversion between the radar coordinate system and the map coordinate system, converting the coordinates of the reflective column in the laser radar coordinate system into the map coordinate system; matching the coordinate of the reflective column in the map coordinate system with the global coordinate of the reflective column; if the successful matching logarithm is greater than or equal to 2, the matching is successful, and the robot pose is calculated by adopting an ICP algorithm, otherwise, the robot pose is predicted based on the odometer data; by adopting the technical scheme, the difficulty of actual deployment of the reflective columns is reduced, the relative position relationship between the reflective columns can be reflected more accurately, the matching error is reduced, the final pose estimation is more accurate, and the overall positioning precision is improved.

Description

Technical Field

[0001] The present application relates to the field of navigation technology, and in particular to a positioning method, storage medium and electronic device based on reflective columns. Background Art

[0002] The reflective column positioning algorithm is a positioning technology based on the characteristics of reflective columns. It is widely used in the fields of autonomous driving, robot navigation, industrial automation, etc. The existing reflective column positioning algorithm mainly identifies the reflective columns set in the environment and uses the characteristic information of the reflective columns scanned by radars such as LiDAR to perform accurate position calculation and positioning. Most of the technologies currently available on the market that use reflective columns for auxiliary positioning use the triangle matching method to match the reflective columns around the robot, and then use the three-sided positioning algorithm to solve the robot's posture.

[0003] However, the existing reflective column positioning algorithm has the following problems:

[0004] First, the arrangement of reflective columns is required to be high; reflective columns should be arranged in positions that can form a stable and easily identifiable triangle, and these positions should be distributed as evenly as possible to reduce matching errors caused by excessive or insufficient spacing between reflective columns;

[0005] Second, the positioning accuracy is seriously affected by the matching effect; the triangle matching method is sensitive to the noise of the input data. In practical applications, due to the influence of factors such as radar accuracy and environmental interference, the input data often contains certain noise, which leads to low algorithm robustness; at the same time, there need to be a large number of similar triangle areas in the actual scene, and the triangle matching algorithm has a high mismatch rate, which is easy to cause positioning jumps;

[0006] Third, the application scenarios are limited; the three-sided positioning algorithm requires at least three known points and they cannot be collinear, which may be difficult to meet in some scenarios, limiting the application scope of the three-sided positioning algorithm. Summary of the invention

[0007] In view of the above problems, the present application provides a positioning method based on reflective columns to solve the problems that the existing positioning method based on reflective columns has high requirements on the arrangement of reflective columns, the positioning accuracy is seriously affected by the matching effect and the application scenarios are limited.

[0008] To achieve the above object, the inventor provides a positioning method based on a reflective column, which comprises the following steps:

[0009] Get the global coordinates of the reflective column;

[0010] Identify and extract reflective columns within the radar FOV range, and obtain the coordinates of the reflective columns in the radar coordinate system;

[0011] Based on the TF transformation between the radar coordinate system and the map coordinate system, the coordinates of the reflective column in the lidar coordinate system are converted to the map coordinate system;

[0012] Match the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column; if the number of successful matches is greater than or equal to 2, the match is successful and the ICP algorithm is used to calculate the robot's posture; otherwise, the robot's posture is predicted based on the odometer data.

[0013] Furthermore, the global coordinates of the reflective column are acquired by traversing and sampling points in the reflective column deployment area.

[0014] Furthermore, in the step of matching the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column, a nearest neighbor matching algorithm is used to match the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column.

[0015] Furthermore, in the step of calculating the robot posture using an ICP algorithm, the ICP algorithm is an ICP algorithm based on the least squares method of CERES.

[0016] Furthermore, the step of predicting the robot's posture based on the odometer data comprises the following steps:

[0017] Based on the timestamp in the odometer data, find the robot pose that is closest in time as the robot pose.

[0018] Furthermore, after the step of calculating the robot posture using the ICP algorithm, it also includes performing positioning jump detection, comparing the robot posture calculated each time with the robot posture calculated in the previous frame or the predicted robot posture, and if the Euclidean distance between the two is greater than a set threshold, it is determined to be a positioning jump, and the predicted robot posture is used to replace the robot posture calculated this time.

[0019] Further, it also includes determining the value of the frame loss calculation;

[0020] If the calculation is successful and the calculated pose does not change compared to the calculated pose or predicted pose of the previous frame, the frame loss calculation is reset; otherwise, the frame loss calculation is performed;

[0021] If the calculated value of consecutive frame loss is greater than the preset loss threshold, positioning is stopped.

[0022] Furthermore, before the step of identifying and extracting the reflective columns within the radar FOV range and obtaining the coordinates of the reflective columns in the radar coordinate system; the method further includes:

[0023] Determine whether a reflective column is detected;

[0024] If yes, determine whether to initialize. If yes, identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system. Otherwise, initialize first, then identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system.

[0025] If not, determine whether to initialize. If so, predict the robot's position based on the odometer data; otherwise, do not perform positioning.

[0026] A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned positioning method based on reflective columns.

[0027] An electronic device comprises a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the above-mentioned positioning method based on reflective columns is implemented.

[0028] Different from the existing technology, the above technical solution matches the coordinates of the reflective columns in the map coordinate system with the global coordinates of the reflective columns. There is no need to deploy the reflective columns at easily identifiable triangular positions, which reduces the difficulty of actual deployment of the reflective columns. At the same time, it can more accurately reflect the relative position relationship between the reflective columns, reduce the matching errors caused by environmental interference, radar errors and other factors, and improve the accuracy and reliability of matching. Even in complex and changeable environments, such as lighting changes and the presence of obstructions, it can still maintain a high level of robustness. The generated matching point pairs are then calculated using the ICP algorithm to calculate the pose. The ICP algorithm iteratively optimizes the distance between matching point pairs to make the final pose estimation more accurate, thereby improving the overall positioning accuracy.

[0029] The above-mentioned records related to the invention content are only an overview of the technical solution of the present application. In order to enable ordinary technicians in the field to more clearly understand the technical solution of the present application, and then implement it according to the text of the specification and the contents recorded in the drawings, and to make the above-mentioned purpose and other purposes, features and advantages of the present application easier to understand, the following is an explanation in combination with the specific implementation mode and drawings of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings are only used to illustrate the principles, implementation methods, applications, characteristics and effects of the specific embodiments of the present invention and other related contents, and shall not be considered as limiting the present application.

[0031] In the drawings of the specification:

[0032] Figure 1 It is a schematic diagram of the process of the positioning method based on reflective columns described in a specific implementation method. DETAILED DESCRIPTION

[0033] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0034] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0035] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0036] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0037] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0038] Without further limitations, in this application, the words "include", "comprises", "has" or other similar open-ended expressions used in sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0039] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0040] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0041] See also Figure 1 As shown, a positioning method based on reflective columns is used to match the coordinates of the reflective columns in the map coordinate system with the global coordinates of the reflective columns. There is no need to deploy the reflective columns at easily identifiable triangular positions, which reduces the difficulty of actual deployment of the reflective columns. At the same time, it can more accurately reflect the relative position relationship between the reflective columns, reduce the matching errors caused by environmental interference, radar errors and other factors, and improve the accuracy and reliability of matching. Even in complex and changeable environments, such as lighting changes and the presence of obstructions, it can still maintain a high robustness; the generated matching point pairs are then calculated using the ICP algorithm. The ICP algorithm iteratively optimizes the distance between the matching point pairs to make the final pose estimation more accurate, thereby improving the overall positioning accuracy; the positioning method can add new reflective columns or update the position information of existing reflective columns to adapt to different application scenarios and needs.

[0042] In the reflective column deployment area, several reflective columns can be distributed in various corners or key positions. There is no need to deploy reflective columns in easily identifiable triangular positions, which reduces the difficulty of actual deployment of reflective columns. Several reflective columns can be evenly distributed or randomly distributed; the even distribution of several reflective columns ensures that the spacing between the reflective columns is moderate, neither too dense nor too sparse. In some embodiments, a grid distribution method can be used, that is, the application scene is divided into multiple grids, and reflective columns are deployed at the intersection or specific position of each grid. The spacing between adjacent reflective columns can be between 1.5-3m.

[0043] The following combination Figure 1 The present application provides an embodiment of a positioning method based on a reflective column, which comprises the following steps:

[0044] Get the global coordinates of the reflective column;

[0045] Identify and extract reflective columns within the radar FOV range, and obtain the coordinates of the reflective columns in the radar coordinate system;

[0046] Based on the TF transformation between the radar coordinate system and the map coordinate system, the coordinates of the reflective column in the lidar coordinate system are converted to the map coordinate system;

[0047] Match the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column; if the number of successful matches is greater than or equal to 2, the match is successful and the ICP algorithm is used to calculate the pose; otherwise, the pose is predicted based on the odometer data.

[0048] The TF transformation between the radar coordinate system and the map coordinate system refers to the pose relationship (position transformation and attitude transformation) in different coordinate systems (radar coordinate system and map coordinate system) at the same time point. The TF transformation can use a transformation matrix to represent the pose relationship in different coordinate systems. The transformation matrix includes a rotation matrix and a translation matrix, which respectively represent the attitude transformation and position transformation between coordinate systems. The coordinates of the reflective column in the map coordinate system are obtained by multiplying the coordinates of the reflective column in the lidar coordinate system by the transformation matrix.

[0049] The global coordinates of the above-mentioned reflective columns can be obtained by traversing and sampling the reflective column deployment area. Starting from the starting point of the deployment area, move step by step according to the predetermined traversal path (such as grid, spiral, etc.). At each traversal point, use the radar to record the global coordinates of the point. Specifically, use the radar to extract nearby high reflection points to obtain the relative position and posture of the reflective column relative to the laser radar; then convert the reflective column posture to the map coordinate system to obtain the reflective column global coordinates, and store the global coordinates of the reflective column, which can be stored in a txt file for subsequent use;

[0050] In the above step of matching the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column, the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column can be matched using a nearest neighbor matching algorithm. Specifically, the following steps are included:

[0051] For each reflective column identified by the radar, the distance between the coordinates of the reflective column in the map coordinate system and all reflective columns in the global coordinates of the reflective column is calculated.

[0052] If the coordinates of the reflective column in the map coordinate system are (x_map, y_map), the coordinates of a reflective column in the global coordinates of the reflective column are (x_global, y_global);

[0053] The distance between the coordinates of the reflective column in the map coordinate system and a reflective column in the global coordinates of the reflective column can be calculated by the Euclidean distance formula d = sqrt ((x_map-x_global)^2+(y_map-y_global)^2). For each reflective column identified by the radar, find the reflective column with the smallest distance in the global coordinates of the reflective column, that is, its nearest neighbor, and record the coordinates of the matching pair and their distance. A distance threshold T can be set. If the distance of the matching pair is less than T, the match is considered successful; otherwise, the match is considered unsuccessful.

[0054] The above-mentioned ICP (Iterative Closest Point) algorithm is used to calculate the robot's posture, that is, the iterative closest point algorithm, which makes the two sets of matching points aligned as much as possible by finding a series of rigid body transformations (rotation and translation). Specifically, based on the corresponding point pairs, an optimal rigid body transformation (rotation and translation) is calculated to transform the coordinates of the reflective column in the map coordinate system to the coordinates of the reflective column paired with it in the global coordinates of the reflective column. This transformation is solved by minimizing the distance error between the two. SVD (singular value decomposition), quaternion method, ceres-based least squares method, etc. are usually used to calculate the optimal rotation matrix and translation vector, so as to accurately locate the robot's posture. The ICP algorithm based on Ceres' least squares method is preferred to calculate the robot's posture, that is, the Ceres-based least squares method is applied to calculate the optimal rotation matrix and translation vector. The Ceres-based least squares method does not rely on the singular value decomposition of the matrix, but finds the optimal solution through iterative optimization. It can better handle special cases such as multiple points in a collinear manner, making the algorithm more robust when processing point cloud data with special geometric features (such as multiple points in a collinear manner), which helps to reduce the solution error in the registration process and improve the registration accuracy.

[0055] The above-mentioned prediction of the robot posture based on the odometer data refers to finding the robot posture closest in time as the robot posture based on the timestamp in the odometer data.

[0056] In order to prevent positioning jumps, after the step of using the ICP algorithm to calculate the robot posture, it also includes positioning jump detection, comparing the robot posture calculated each time with the robot posture calculated in the previous frame or the predicted robot posture. If the Euclidean distance between the two is greater than the set threshold, it is determined to be a positioning jump, and the robot posture calculated in the previous frame or the predicted robot posture is used to replace the robot posture calculated at that time. By detecting and processing positioning jumps, sudden position offsets caused by environmental noise (such as sudden changes in lighting, obstructions, etc.), radar errors or algorithm instability can be significantly reduced, thereby improving the continuity and stability of robot positioning; at the same time, it can effectively reduce this unnecessary resource consumption (positioning jumps may cause the robot to perform unnecessary re-positioning or path planning), and improve the overall efficiency of the system.

[0057] In order to reduce positioning errors, it also includes the value of the frame loss calculation;

[0058] If the calculation is successful and the calculated pose does not change compared to the calculated pose or predicted pose of the previous frame, the frame loss calculation is reset; otherwise, the frame loss calculation is performed;

[0059] If the calculated value of consecutive frame loss is greater than the preset loss threshold, positioning is stopped.

[0060] By monitoring the frame loss, the system can promptly detect anomalies in data transmission or processing, thereby avoiding the use of erroneous or incomplete data for positioning calculations, which helps reduce positioning errors and improve the overall reliability of the positioning system. When the value of the continuous frame loss calculation is less than the preset loss threshold, the robot's position and posture can be predicted based on the odometer data, so that the robot can still maintain positioning accuracy when encountering short-term data loss or processing delays, thereby enhancing the robustness of positioning. When the value of the continuous frame loss calculation is greater than the preset loss threshold, positioning is stopped, providing a preventive measure to prevent complete positioning failure due to severe data loss or processing failure, protecting the safety of the robot and the surrounding environment, and avoiding potential collisions or damage.

[0061] Before the step of identifying and extracting the reflective column within the radar FOV range and obtaining the coordinates of the reflective column in the radar coordinate system; it usually also includes determining whether the reflective column is detected;

[0062] If yes, determine whether to initialize. If yes, identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system. Otherwise, initialize first, then identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system.

[0063] If not, determine whether to initialize. If so, predict the robot's position based on the odometer data; otherwise, do not perform positioning.

[0064] The above line is initialized to record the odometer data of the first frame and the TF transformation between the laser and the map coordinate system. The above prediction of the robot posture based on the odometer data also refers to finding the robot posture closest in time as the robot posture based on the timestamp in the odometer data.

[0065] The present application also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned positioning method based on reflective columns is implemented.

[0066] The computer program involved in the embodiment can be stored in a computer device readable storage medium, which includes but is not limited to a disk, a tape, a magnetic card, a floppy disk, a flash memory, an optical disk, an optical card, a read-only memory (ROM), a random access memory (RAM), an erasable programmable ROM (EPROM) and an electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, protein and other units with information storage capabilities. In a specific embodiment, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiment can be stored in a single medium in a centralized manner, or it can be stored in multiple media in a distributed manner. The memory containing the computer device readable storage medium can be a non-volatile memory or a random access memory. These computer device readable storage media can be built into the device, or they can be connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, a memory with a computer device readable storage medium is deployed locally; in other embodiments, a solution of deploying the memory away from the processor may also be adopted, such as a network attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), etc., or a suitable combination thereof, as long as the computer device can access the memory. In addition, the computer program involved in the embodiment may be stored in plaintext / ciphertext form, or may be designed as training data, which may be integrated and reorganized through model training and implicitly stored in the parameter state of a deep neural network or other robot learning model.

[0067] The present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the above-mentioned positioning method based on reflective columns is implemented.

[0068] The processor described in the embodiments of the present application can be implemented by hardware, firmware, software or a combination thereof, and can use a circuit, a single or multiple application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable Gate Array, FPGA), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, at least one of a microprocessor, and also includes other physical, biological or chemical structures that can achieve similar or equivalent functions to the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of the present application, or any combination of the steps mentioned therein.

[0069] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A positioning method based on reflective columns, characterized in that: The following steps are involved: Get the global coordinates of the reflective column; Identify and extract reflective columns within the radar FOV range, and obtain the coordinates of the reflective columns in the radar coordinate system; Based on the TF transformation between the radar coordinate system and the map coordinate system, the coordinates of the reflective column in the lidar coordinate system are converted to the map coordinate system; Match the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column; if the number of successful matches is greater than or equal to 2, the match is successful and the ICP algorithm is used to calculate the robot's posture; otherwise, the robot's posture is predicted based on the odometer data.

2. The positioning method based on reflective columns according to claim 1, characterized in that: The global coordinates of the reflective column are obtained by traversing and sampling points in the reflective column deployment area.

3. The positioning method based on reflective columns according to claim 1, characterized in that: In the step of matching the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column, a nearest neighbor matching algorithm is used to match the coordinates of the reflective column in the map coordinate system with the global coordinates of the reflective column.

4. The positioning method based on reflective columns according to claim 1, characterized in that: In the step of using the ICP algorithm to calculate the robot's posture, the ICP algorithm is an ICP algorithm based on the least squares method of ceres.

5. The positioning method based on reflective columns according to claim 1, characterized in that: The step of predicting the robot's posture based on the odometer data comprises the following steps: Based on the timestamp in the odometer data, find the robot pose that is closest in time as the robot pose.

6. The positioning method based on reflective columns according to claim 1, characterized in that: It also includes, after the step of calculating the robot posture using the ICP algorithm, performing positioning jump detection, comparing the robot posture calculated each time with the robot posture calculated in the previous frame or the predicted robot posture, and if the Euclidean distance between the two is greater than a set threshold, it is determined to be a positioning jump, and the predicted robot posture is used to replace the robot posture calculated this time.

7. The positioning method based on reflective columns according to claim 6, characterized in that: It also includes the value used to determine the frame loss calculation; If the calculation is successful and the calculated pose does not change compared to the calculated pose or predicted pose of the previous frame, the frame loss calculation is reset; otherwise, the frame loss calculation is performed; If the calculated value of consecutive frame loss is greater than the preset loss threshold, positioning is stopped.

8. The positioning method based on reflective columns according to claim 1, characterized in that: Before the step of identifying and extracting the reflective columns within the radar FOV range and obtaining the coordinates of the reflective columns in the radar coordinate system; the step also includes: Determine whether a reflective column is detected; If yes, determine whether to initialize. If yes, identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system. Otherwise, initialize first, then identify and extract the reflective column within the radar FOV range, and obtain the coordinates of the reflective column in the radar coordinate system. If not, determine whether to initialize. If so, predict the robot's position based on the odometer data; otherwise, do not perform positioning.

9. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the positioning method based on reflective columns as described in any one of claims 1 to 8.

10. An electronic device, characterized in that It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the positioning method based on reflective columns as described in any one of claims 1 to 8 is implemented.

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