Positioning method, device, electronic device and computer storage medium

Abstract

The embodiments of the present application provide a positioning method, apparatus, electronic device, and computer storage medium. The positioning method includes: obtaining a frame-relative pose for mapping a current point cloud frame collected by a lidar to a local map, a map-absolute pose for mapping the local map to a global map, and a frame-absolute pose for mapping the current point cloud frame to a global map; performing pose fusion on the frame-absolute pose, the map-absolute pose, and the frame-relative pose to obtain a final absolute pose corresponding to the current point cloud frame; and determining, based on the final absolute pose and the current point cloud frame, abnormal global points in the global map whose position change is greater than or equal to a screening threshold, so as to determine the frame-absolute pose of the next point cloud frame based on the global map in which the abnormal global points are identified. This positioning method can obtain a more reliable positioning output.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a positioning method, device, electronic device, and computer storage medium. Background Art

[0002] The positioning unit is a fundamental unit in intelligent driving systems. It provides real-time global position information for autonomous vehicles. LiDAR positioning is a very important subunit of the positioning unit. Most laser positioning solutions use absolute positioning, which has the following disadvantages:

[0003] 1) The inter-frame positioning error fluctuates greatly and the pose is not smooth enough;

[0004] 2) In scenes with relatively open space or changing environments, the positioning errors of some frames are large. Summary of the Invention

[0005] In view of this, an embodiment of the present application provides a positioning solution to at least partially solve the above-mentioned problem.

[0006] According to a first aspect of an embodiment of the present application, a positioning method is provided, including: obtaining a frame relative pose for mapping a current point cloud frame collected by a lidar to a local map, a map absolute pose for mapping the local map to the global map, and a frame absolute pose for mapping the current point cloud frame to the global map; performing pose fusion on the frame absolute pose, the map absolute pose and the frame relative pose to obtain a final absolute pose corresponding to the current point cloud frame; determining, based on the final absolute pose and the current point cloud frame, an abnormal global point in the global map whose position change is greater than or equal to a screening threshold, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global point is identified.

[0007] According to a second aspect of an embodiment of the present application, a positioning device is provided, including: a first acquisition module for acquiring a frame relative pose for mapping a current point cloud frame collected by a lidar to a local map, a map absolute pose for mapping the local map to the global map, and a frame absolute pose for mapping the current point cloud frame to the global map; a fusion module for performing pose fusion on the frame absolute pose, the map absolute pose and the frame relative pose to obtain a final absolute pose corresponding to the current point cloud frame; an evaluation module for determining, based on the final absolute pose and the current point cloud frame, an abnormal global point in the global map whose position change degree is greater than or equal to a screening threshold, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global point is identified.

[0008] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the positioning method described in the first aspect.

[0009] According to a fourth aspect of the embodiments of the present application, a computer storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the positioning method as described in the first aspect is implemented.

[0010] According to the positioning solution provided in the embodiments of the present application, when determining the final absolute pose, the frame-relative pose and the map-absolute pose are fused into the frame-absolute pose, thereby making the fused final absolute pose more accurate and reducing error fluctuation and maximum error. Moreover, based on the final absolute pose, the current point cloud frame can be mapped to the global map to determine the degree of position change of each global point in the global map, thereby determining abnormal global points caused by environmental changes, and avoiding interference from abnormal global points when determining the frame-absolute pose of the next point cloud frame, thereby avoiding positioning failures caused by environmental changes and improving robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0012] Figure 1A This is a flowchart of the steps of a positioning method according to the first embodiment of the present application;

[0013] Figure 1B for Figure 1A A schematic diagram of an example scenario in the illustrated embodiment;

[0014] Figure 2A This is a flowchart of the steps of a positioning method according to the second embodiment of the present application;

[0015] Figure 2B for Figure 2A A schematic diagram of a laser positioning framework in the illustrated embodiment;

[0016] Figure 3 This is a structural block diagram of a positioning device according to the third embodiment of the present application;

[0017] Figure 4This is a structural diagram of an electronic device according to the fourth embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field should fall within the scope of protection of the embodiments of the present application.

[0019] The specific implementation of the embodiment of the present application is further explained below in conjunction with the accompanying drawings of the embodiment of the present application.

[0020] Example 1

[0021] Reference Figure 1A , shows a schematic flow chart of the steps of the positioning method of Example 1 of the present application.

[0022] In this embodiment, the method can be applied to the field of intelligent driving to achieve vehicle positioning, so that navigation or driving control can be performed based on the vehicle positioning information. The method includes the following steps:

[0023] Step S102: Obtain a frame relative pose for mapping a current point cloud frame collected by a lidar to a local map, a map absolute pose for mapping the local map to the global map, and a frame absolute pose for mapping the current point cloud frame to the global map.

[0024] The current point cloud frame may be information about reflection points in the environment collected by the laser radar at the current moment. For example, the current point cloud frame includes 3D position information of multiple reflection points in the environment at the current moment.

[0025] Figure 1B Figure 1 shows global space 1. A lidar is mounted on a vehicle and moves from point A to point B in global space 1. During this process, the lidar collects a point cloud frame at regular intervals. For example, if the current moment is time t, the point cloud frame collected at time t is the current point cloud frame.

[0026] A local map can be constructed based on at least some of the point cloud frames collected by the lidar. The local map includes location information of multiple local points. The location information of the local points can be determined based on the location information of the reflection points in the point cloud frames used to construct the local map.

[0027] In this embodiment, the collected current point cloud frame can be input into the laser odometry, and the frame relative pose of the current point cloud frame to the local map can be determined by the laser odometry. The current point cloud frame can be mapped to the local map through the frame relative pose.

[0028] The global map includes the location information of multiple global points. These global points can be points acquired through data collection by a LiDAR radar corresponding to global space 1. In this embodiment, the global map is an a priori map, that is, a priori map acquired in advance by a LiDAR vehicle. In this embodiment, the location information of the global points in the a priori map does not change. However, the location information of the global points in the a priori map can be updated at regular intervals (e.g., half a month, one month, or six months) as needed.

[0029] The map absolute pose is used to map the local map to the global map. In one feasible approach, the local map can be input into a global absolute positioning unit, and the map absolute pose is determined by the global absolute positioning unit.

[0030] The frame absolute pose is used to map the current point cloud frame to the global map. In one feasible approach, the current point cloud frame can be input into a global absolute positioning unit, and the frame absolute pose is determined by the global absolute positioning unit.

[0031] The global absolute positioning unit can adopt an appropriate unit that can realize the corresponding function as needed.

[0032] Step S104: performing posture fusion on the frame absolute posture, the map absolute posture and the frame relative posture to obtain a final absolute posture corresponding to the current point cloud frame.

[0033] In this embodiment, a graph optimization approach can be used to fuse the frame absolute pose, map absolute pose, and frame relative pose. This uses the map absolute pose and frame relative pose as constraints, making the resulting fused absolute pose more accurate. Furthermore, because the final absolute pose incorporates the frame relative pose and map absolute pose, the maximum error of the final absolute pose can be effectively reduced, and error fluctuations can be mitigated, resulting in a more accurate final absolute pose for the current point cloud frame.

[0034] Step S106: Determine, based on the final absolute pose and the current point cloud frame, abnormal global points in the global map whose position change is greater than or equal to a screening threshold, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global points are identified.

[0035] Based on the determined final absolute pose, each reflection point in the current point cloud frame can be mapped to the global map. For each reflection point, the second offset distance between the reflection point and the nearest global point can be determined according to the position of the reflection point mapped to the global map. The second offset distance can be used as the degree of position change of the global point. The screening threshold can be determined according to the degree of position change of each global point. The specific method for determining the screening threshold can be determined as needed.

[0036] Based on the screening threshold, abnormal global points whose position changes are greater than or equal to the screening threshold can be determined from the global points. In this way, environmental changes in the global map can be discovered in a timely manner. Because different reflection points will appear when the environment changes, based on the current point cloud frame collected in real time, it can be determined whether the real environment corresponding to the global map has changed.

[0037] In this way, environmental changes can be determined in a timely manner, and the global points corresponding to the changed environment (that is, abnormal global points) can be determined from the global map. In this way, when determining the absolute pose of the frame corresponding to the next point cloud frame, the influence of abnormal global points can be avoided, thereby ensuring that the final absolute pose can be accurately determined even in the case of environmental changes, thereby achieving robust positioning.

[0038] The following describes the implementation process of the positioning method in combination with a specific usage scenario:

[0039] A global map in global space 1 is obtained in advance. For example, a laser radar vehicle travels in global space 1 and collects three-dimensional position information of reflection points. These reflection points collected by the laser radar vehicle serve as global points in the global map.

[0040] When positioning based on a global map, the vehicle's LiDAR collects information about reflection points in the environment as the current point cloud frame. This point cloud frame includes information about reflection points on target objects in the environment, such as their position and emission values.

[0041] On the one hand, the current point cloud frame is input into the laser odometry to obtain the frame relative pose for mapping the current point cloud frame to the local map; on the other hand, the current point cloud frame and the local map can be input into the global absolute positioning unit to obtain the frame absolute pose corresponding to the current point cloud frame and the map absolute pose corresponding to the local map.

[0042] Through the pose fusion unit, the frame absolute pose, map absolute pose and frame relative pose are fused to obtain a more accurate final absolute pose. The final absolute pose is used to map the current point cloud frame to the global map.

[0043] After mapping the current point cloud frame to the global map, the distance between the global point of the global map and the corresponding reflection point can be determined according to the mapping result, so as to determine the degree of position change of the global point, and thus determine the abnormal global points among the global points whose degree of position change is greater than or equal to the screening threshold (the screening threshold can be determined as needed). Then, when determining the absolute pose of the next frame of point cloud, the global map with the abnormal global points marked can be used as the basis, thereby ensuring that pose estimation can be performed well when the environment changes, thereby reducing the situation of positioning failure caused by environmental changes.

[0044] Through this embodiment, when determining the final absolute pose, the frame-relative pose and the map-absolute pose are fused into the frame-absolute pose, making the fused final absolute pose more accurate and reducing error fluctuation and maximum error. Moreover, based on the final absolute pose, the current point cloud frame can be mapped to the global map, thereby determining the degree of position change of each global point in the global map, and identifying abnormal global points caused by environmental changes. This can avoid interference from abnormal global points when determining the frame-absolute pose of the next point cloud frame, thereby avoiding positioning failures caused by environmental changes and improving robustness.

[0045] The positioning method of this embodiment can be executed by any appropriate electronic device with positioning capability, including but not limited to: a server, a mobile terminal (such as a mobile phone, a PAD, etc.) and a PC.

[0046] Example 2

[0047] Reference Figure 2A , shows a schematic flow chart of the steps of the positioning method of Example 2 of the present application.

[0048] In this embodiment, the positioning method includes the following steps:

[0049] Step S202: Obtain a frame relative pose for mapping the current point cloud frame collected by the lidar to the local map.

[0050] In one specific implementation, the current point cloud frame captured by the lidar is input into the laser odometry, which then matches the current point cloud frame to the local map, obtaining the frame-relative pose. This allows the local map to determine the first reference pose in the set local coordinate system and the second reference pose of the current point cloud frame in the local coordinate system. During this process, the laser odometry can also perform operations such as selecting key point cloud frames and updating the local map.

[0051] The local map (also called a submap) is composed of multiple keypoint cloud frames. For example, the reflection points in the keypoint cloud frames are mapped to the local map according to the relative pose of each keypoint cloud frame. Based on the mapped position of the reflection points and the position information of the local points in the local map, the reflection points and local points are clustered together to obtain multiple clusters. The centroid of each cluster is then calculated and used as the new local point, and the position information of the centroid is used as the new local point.

[0052] Based on this, step S202 includes the following process:

[0053] Process A1: Determine whether the current point cloud frame is a key point cloud frame.

[0054] Since the local map is constructed through key point cloud frames, it is necessary to determine whether the current point cloud frame collected is a key point cloud frame.

[0055] One way to determine whether a point cloud frame is a key point cloud frame may be to sum the frame relative pose of the point cloud frame preceding the current point cloud frame with a preset adjustment amount, and use the summed result as the initial frame relative pose of the current point cloud frame. If the position difference or posture difference between the initial frame relative pose of the current point cloud frame and the frame relative pose corresponding to the last key point cloud frame in the local map is greater than or equal to a set posture difference threshold (which can be determined based on experience or needs and is not limited in this embodiment), the current point cloud frame is determined to be a key point cloud frame. Otherwise, the current point cloud frame is not determined to be a key point cloud frame.

[0056] If the current point cloud frame is a key point cloud frame, process B1 is executed; otherwise, process C1 is executed.

[0057] Process B1: Use the current point cloud frame as the key point cloud frame to update the local map.

[0058] Case 1: If the number of key point cloud frames in the currently used local map is not greater than the split value, the current point cloud frame is directly added to the currently used local map, and the local points in the local map and their position information are calculated using the aforementioned method.

[0059] Case 2: If the number of key point cloud frames in the currently used local map is greater than the split value but less than the total capacity value (this value can be determined based on positioning capabilities, storage capabilities, etc., such as 10 frames, 20 frames, 50 frames, etc.), then the current point cloud frame can be added to the currently used local map, and the local points and their location information in the local map are calculated using the aforementioned method. At the same time, the current point cloud frame is added to the backup local map.

[0060] Case 3: If the number of key point cloud frames in the currently used local map is equal to the total capacity value, the spare local map is used as the new currently used local map, and it is determined whether the new currently used local map belongs to case 1 or 2. If the new currently used local map belongs to case 1, refer to the above case 1 and add the current point cloud frame to the new currently used local map before performing subsequent operations.

[0061] Process C1 can be executed after the local map is updated.

[0062] Process C1: Determine the frame relative pose based on the local map corresponding to the current point cloud frame.

[0063] In one feasible approach, multiple reflection points in the current point cloud frame are mapped to a local map based on the relative pose of the initial frame of the current point cloud frame. The mapped position of each reflection point is then determined based on the k nearest local points in the local map for each reflection point. A reference surface for the reflection point is constructed using these k nearest local points, and the distance from the reflection point to the corresponding reference surface is calculated.

[0064] Calculate the sum of the squares of the distances from each reflection point to the corresponding reference surface, and optimize the initial frame relative pose according to the Gauss-Newton iteration method based on the sum of the squares. Then map the current point cloud frame to the local map with the optimized frame relative pose. Calculate the sum of the squares of the distances from each reflection point to the corresponding reference surface, and optimize the frame relative pose according to the Gauss-Newton iteration method based on the sum of the squares. Repeat the iterative optimization until convergence, and then use the optimized frame relative pose at convergence as the frame relative pose of the current point cloud frame.

[0065] The convergence condition may be that the sum of two adjacent squares is equal or the difference is less than a set value (the set value can be determined as needed).

[0066] Optionally, in addition to obtaining the frame-relative pose of the current point cloud frame, step S202 can also obtain the first reference pose of the local map in the set local coordinate system and the second reference pose of the current point cloud frame in the local coordinate system. The first reference pose and the second reference pose can make the subsequent determination of the absolute pose more accurate.

[0067] The local coordinate system can be a coordinate system selected as needed. The local coordinate system corresponds to the laser odometry (or laser radar). For example, the frame relative pose corresponding to the first point cloud frame collected by the laser radar can be used as the pose corresponding to the local coordinate system, or the pose of the global coordinate system corresponding to the global map can be used as the pose of the local coordinate system, or any pose can be selected as the pose of the local coordinate system.

[0068] Once the pose of the local coordinate system is determined, the transformation relationship between the local coordinate system and the global coordinate system can be determined. Of course, since the pose of the local coordinate system is the frame-relative pose of the first point cloud frame of the LiDAR, and there may be errors in the frame-relative pose, the pose of the local coordinate system can be optimized, and the transformation relationship between the local coordinate system and the global coordinate system will also change with the optimization.

[0069] After determining the pose of the local coordinate system, the first reference pose of the local map in the local coordinate system can be determined through process D1, and the second reference pose of the current point cloud frame in the local coordinate system can be determined through process E1.

[0070] Process D1: Determine a first reference pose of the local map in the local coordinate system according to a frame-relative pose of at least one key point cloud frame in the local map.

[0071] In one feasible approach, a first reference pose is determined based on a frame-relative pose of a first key point cloud frame in the local map and a pose of a local coordinate system.

[0072] Process E1: Determine a second reference pose of the current point cloud frame in the local coordinate system.

[0073] In one feasible manner, the second reference pose is obtained by multiplying the frame relative pose of the current point cloud frame and the first reference pose.

[0074] Step S204: Obtaining a map absolute pose for mapping the local map into the global map.

[0075] In one feasible manner, step S204 includes the following sub-steps:

[0076] Sub-step S2041: Determine a map predicted absolute pose for mapping the local map into the global map.

[0077] For example, sub-step S2041 includes the following process:

[0078] Process A2: Obtaining the frame absolute pose corresponding to the previous point cloud frame, the second reference pose of the previous point cloud frame in the local coordinate system, and the first reference pose for mapping the local map into the local coordinate system.

[0079] The absolute pose of the frame corresponding to the previous point cloud frame can be recorded as This is the pose that has been calculated, so it will not be described in detail. The second reference pose of the previous point cloud frame in the local coordinate system can be recorded as This is a calculated value, so it will not be repeated here. The first reference pose used to map the local map to the local coordinate system can be written as It can be determined through the aforementioned process D1, so it will not be described in detail.

[0080] Process B2: Determine the map predicted absolute pose corresponding to the local map based on the first reference pose, the frame absolute pose corresponding to the previous point cloud frame, and the second reference pose of the previous point cloud frame.

[0081] In one feasible approach, the product of the first reference pose, the frame absolute pose corresponding to the previous point cloud frame, and the second reference pose of the previous point cloud frame can be used as the map-predicted absolute pose. The map-predicted absolute pose is expressed as:

[0082] Sub-step S2042: Predicting an absolute pose according to the map, matching the local map with the global map to determine a map absolute pose for mapping the local map into the global map.

[0083] In one feasible approach, a global absolute positioning unit may be used to match a local map with a global map based on a predicted map pose, thereby determining a map absolute pose for mapping the local map into the global map.

[0084] For example, local points in the local map are mapped to the global map according to the predicted pose. Based on the mapped position of the local points, the k nearest global points are determined. For each local point, the k nearest global points are filtered out based on the abnormal global points in the global map corresponding to the current point cloud frame. For example, for the k nearest global points corresponding to local point A, the abnormal global points are removed.

[0085] If the number of remaining normal global points is greater than or equal to 3 and can constitute a reference surface, the distances between these local points and the reference surface are calculated, and based on the sum of the squares of the distances of these local points, the predicted map pose is optimized using the Gauss-Newton iteration method to obtain the optimized absolute map pose.

[0086] Then, the local map is mapped to the global coordinate system according to the optimized map absolute pose. The above process of determining the k nearest global points based on the mapped position of the local point is repeated. The map absolute pose is iteratively optimized until the convergence condition is met. The pose thus optimized is the map absolute pose.

[0087] Step S206: Obtaining a frame absolute pose for mapping the current point cloud frame to the global map.

[0088] In one feasible approach, step S206 includes the following sub-steps:

[0089] Sub-step S2061: Determine the frame predicted absolute pose for mapping the current point cloud frame to the global map based on the final absolute pose corresponding to the previous point cloud frame collected by the lidar, the second reference pose of the previous point cloud frame in the local coordinate system, and the second reference pose of the current point cloud frame in the local coordinate system.

[0090] The final absolute pose corresponding to the previous point cloud frame can be recorded as Since it is a pose that has already been calculated, it will not be described in detail.

[0091] The second reference pose of the previous point cloud frame can be recorded as Since it is a pose that has already been calculated, it will not be described in detail.

[0092] The second reference pose of the current point cloud frame in the local coordinate system is denoted as It can be determined through the aforementioned process E1, so it will not be described in detail.

[0093] The frame predicted absolute pose for mapping the current point cloud frame to the global map can be the product of the three, which can be expressed as:

[0094] Sub-step S2062: determining normal reflection points and abnormal reflection points among the reflection points included in the current point cloud frame.

[0095] The abnormal reflection point is a reflection point whose first offset distance from a corresponding global point in the global map is greater than or equal to a change threshold.

[0096] The change threshold can be determined based on needs or experience, and this embodiment does not limit this.

[0097] Sub-step S2062 includes the following process:

[0098] Process A3: Obtain an inter-frame displacement increment, and determine a predicted absolute pose corresponding to the current point cloud frame based on the inter-frame displacement increment and the final absolute pose corresponding to the previous point cloud frame collected by the lidar.

[0099] The inter-frame displacement increment can be determined based on needs or experience.

[0100] The abnormal screening absolute pose corresponding to the current point cloud frame may be the sum of the inter-frame displacement increment and the final absolute pose corresponding to the previous point cloud frame acquired by the lidar.

[0101] Process B3: determining a first offset distance between a reflection point of the current point cloud frame and a corresponding global point in the global map according to the predicted absolute pose and the global map corresponding to the current point cloud frame.

[0102] The reflection points of the current point cloud frame are mapped to the global map according to the abnormal screening absolute pose. According to the mapping position of each reflection point, the nearest global point of each reflection point is determined from the global points of the global map, and the first offset distance between each reflection point and the corresponding nearest global point is calculated.

[0103] Process C3: Determine and identify abnormal reflection points in the current point cloud frame based on the first offset distance of each reflection point

[0104] If the first offset distance is greater than or equal to the change threshold, it is determined as an abnormal reflection point.

[0105] Since abnormal reflection points may be caused by environmental changes, which will have an adverse effect on positioning accuracy, abnormal reflection points are removed when determining the absolute pose of the frame to ensure accuracy.

[0106] Sub-step S2063: Predicting the absolute pose of the frame, matching the normal reflection points other than the abnormal reflection points in the current point cloud frame with the global map, and determining the frame absolute pose corresponding to the current point cloud frame according to the matching result.

[0107] For example, sub-step S2063 includes the following process:

[0108] Process A4: predicting the absolute pose according to the frame, mapping each normal reflection point in the current point cloud frame to the global map to obtain a first mapping position of each normal reflection point.

[0109] For example, multiply each normal reflection point in the current point cloud frame by the yaw angle, pitch angle, and roll angle in the frame predicted absolute pose, and add the offset of the x-axis, y-axis, and z-axis. The result is the first mapping position in the global map.

[0110] Process B4: determining k adjacent global points corresponding to each normal reflection point according to the first mapping position of each normal reflection point and the position of the global point in the global map.

[0111] According to the first mapping position, k neighboring global points are obtained from the global points included in the global map. These k neighboring global points may or may not include the abnormal global point. The abnormal global point is determined based on the global map corresponding to the current point cloud frame.

[0112] Process C4: for each normal reflection point, filter out abnormal global points from the k neighboring global points corresponding to the normal reflection point, and determine the surface offset distance of each normal reflection point relative to the reference surface constructed by the remaining normal global points.

[0113] For example, for a normal reflection point M, if its corresponding k neighboring global points include an abnormal global point, the abnormal global point is screened out.

[0114] If the number of normal global points remaining after screening is greater than or equal to 3, the reference surface is determined based on the remaining normal global points, and the surface offset distance between the normal reflection point and the reference surface is calculated.

[0115] Alternatively, if the number of normal global points remaining after screening is less than 3, the normal reflection point can be abandoned because it cannot constitute a reference surface.

[0116] Process D4: determining the absolute frame pose corresponding to the current point cloud frame according to the surface offset distances.

[0117] In one feasible approach, the frame-predicted absolute pose is optimized using a Gauss-Newton iteration method based on the sum of the squared offset distances of each face, obtaining an optimized frame-absolute pose. This optimized frame-absolute pose is then used for iterative optimization, ultimately obtaining the frame-absolute pose corresponding to the current point cloud frame. The iterative optimization process can return to process A4 and replace the frame-predicted absolute pose with the optimized frame-absolute pose, continuing until a converged frame-absolute pose is obtained. A converged frame-absolute pose can be achieved when the deviation between two consecutive optimized frame-absolute poses is less than or equal to a certain value.

[0118] Step S208: performing posture fusion on the frame absolute posture, the map absolute posture and the frame relative posture to obtain a final absolute posture corresponding to the current point cloud frame.

[0119] Step S208 includes the following process:

[0120] Process A5: Obtain a first reference pose of the local map in a set local coordinate system, a second reference pose of the current point cloud frame in the local coordinate system, and a conversion relationship between the local coordinate system and the global coordinate system corresponding to the global map.

[0121] The first reference pose can be expressed as The pose obtained by the above process can be used, so I will not go into details.

[0122] The second reference pose can be expressed as The pose obtained by the above process can be used, so I will not go into details.

[0123] The conversion relationship can be expressed as When the position and orientation of the local coordinate system are determined, the transformation relationship is known, so it will not be described in detail.

[0124] Process B5: constructing a fused graph optimization problem according to the frame absolute pose, the map absolute pose and the frame relative pose according to graph optimization rules.

[0125] The absolute pose of the frame can be expressed as The pose obtained by the above process can be used, so I will not go into details.

[0126] The absolute pose of the map can be expressed as The pose obtained by the above process can be used, so I will not go into details.

[0127] The frame relative pose can be expressed as The pose obtained by the above process can be used, so I will not go into details.

[0128] The graph optimization rules may adopt any appropriate rules, so they will not be described in detail.

[0129] Process C5: Solving the graph optimization problem according to the first reference pose, the second reference pose and the transformation relationship to obtain a final absolute pose of the fusion corresponding to the current point cloud frame.

[0130] In one feasible manner, the first reference pose, the second reference pose and the conversion relationship are optimized, and the final frame absolute pose is determined based on the optimized first reference pose, the second reference pose and the conversion relationship.

[0131] Optimization process, for example:

[0132] On the one hand, the intermediate frame relative pose is calculated according to the first reference pose and the second reference pose, and then the difference between the intermediate frame relative pose and the frame relative pose is calculated, and then the first reference pose and the second reference pose are optimized according to the difference.

[0133] On the other hand, the intermediate map absolute pose is calculated based on the first reference pose and the transformation relationship, and then the difference between the intermediate map absolute pose and the map absolute pose is calculated, and then the first reference pose and the transformation relationship are optimized based on the difference.

[0134] On the other hand, the absolute pose of the intermediate frame is calculated based on the second reference pose and the transformation relationship, and then the difference between the absolute pose of the intermediate frame and the absolute pose of the frame is calculated, and then the second reference pose and the transformation relationship are optimized based on the difference.

[0135] In this way, the three aspects are jointly optimized until the first reference pose, the second reference pose and the transformation relationship with the smallest three differences are obtained.

[0136] When the final absolute pose is determined based on the optimized first reference pose, second reference pose and transformation relationship, the final absolute pose of the key point cloud frame is expressed as

[0137] The final absolute pose of the non-keypoint cloud frame is expressed as

[0138] Step S210: Mapping the current point cloud frame to the global map according to the final absolute pose to obtain a second mapping position of each reflection point in the current point cloud frame in the global map.

[0139] Step S212: determining a second offset distance between each reflection point and the corresponding nearest global point in the global map according to the second mapping position of each reflection point and the nearest global point corresponding to each reflection point in the global map.

[0140] Step S214: Determine the confidence level of the final absolute pose using a normalization function according to the second offset distance of the reflection point corresponding to each global point.

[0141] In one feasible method, the second offset distance of each reflection point is summed to obtain the second offset distance sum, and a normalization function (such as a softmax function) is used to normalize the second offset distance sum so that it is normalized to between [0, 1], thereby obtaining the confidence of the final absolute pose.

[0142] The confidence level can be calculated by a confidence level evaluation unit, and the reliability of positioning can be evaluated based on the confidence level.

[0143] Step S216: determining abnormal global points in the global map whose position change degree is greater than or equal to a screening threshold based on the final absolute pose and the current point cloud frame.

[0144] In one feasible approach, step S216 includes the following process:

[0145] Process A6: Mapping the reflection point of the current point cloud frame to the global map according to the final absolute pose to obtain a second mapping position of the reflection point in the global map.

[0146] Process B6: determining a second offset distance between each reflection point and the corresponding nearest global point in the global map according to the second mapping position of each reflection point.

[0147] For example, for each reflection point, a global point with the smallest distance from the reflection point is selected from the global points, and the distance between the reflection point and the selected global point is used as the second offset distance.

[0148] Process C6: determining abnormal global points whose position change degree is greater than or equal to a screening threshold according to the second offset distance of the reflection point corresponding to each global point.

[0149] For example, a global map includes global points 1 to N, where N is greater than 1. P of the global points have corresponding reflection points, and the position change degrees of these P global points can be the second offset distances of the corresponding reflection points. The position change degrees of the remaining global points can be determined to be 0.

[0150] Based on the position change degree of each global point, the number of global points distributed in different position change degree intervals can be determined, and then a screening threshold is determined based on the distribution. Based on the screening threshold, global points with a position change degree greater than or equal to the screening threshold are identified as abnormal global points.

[0151] This method allows for the identification of outlier global points in the global map based on the current point cloud frame and the corresponding final absolute pose, allowing for timely determination of environmental changes. These outlier global points can be applied to the next point cloud frame, for example, when determining the frame absolute pose of the next point cloud frame and the map absolute pose of the local map corresponding to the next point cloud frame. This ensures that outlier global points are screened out when calculating the absolute pose, thereby avoiding positioning failures due to environmental changes and reducing the error fluctuation range and maximum error.

[0152] like Figure 2B As shown in Figure 1, this method can be implemented in a robust laser positioning framework that includes a laser odometry, a global absolute positioning unit, an environmental risk assessment unit, and a positioning confidence assessment unit. This method provides a continuously stable and reliable positioning output, namely, the final absolute pose of the current point cloud frame.

[0153] The introduction of an environmental risk assessment unit and a positioning reliability assessment unit allows for timely and accurate identification of environmental changes, significantly reducing positioning failures due to these changes. The positioning reliability assessment unit provides an estimate of the reliability of the current final absolute position. The final absolute position is determined by matching the local map with the global map, known as the map absolute position. This matching is beneficial in situations such as environmental changes and short-term map outages, effectively improving positioning reliability.

[0154] Among them, the laser odometry provides the frame-relative pose information of the current point cloud frame relative to the local map; the global absolute positioning unit provides the frame-absolute pose of the current point cloud frame relative to the global map, and the map-absolute pose of the local map relative to the global map.

[0155] The pose fusion unit is used to fuse the frame relative pose output by the laser odometry, the frame absolute pose output by the global absolute positioning unit, and the map absolute pose to output the final absolute pose.

[0156] The risk assessment unit can detect abnormal reflection points of the current point cloud frame relative to the prior global map and the degree of position change of each global point in the prior global map, and then determine the abnormal global point.

[0157] The global absolute positioning unit applies these abnormal reflection points and abnormal global points to the frame absolute pose of the current point cloud frame and the map absolute pose of the local map, and sets lower weights for the abnormal reflection points and abnormal global points (such as screening out these abnormal points) to reduce the negative impact of environmental changes on global positioning, thereby improving reliability.

[0158] This method can solve the problem of no final confidence output and inability to judge the quality of positioning results. In addition, environmental change detection can feed back change information into absolute pose determination, allowing for better positioning for large-scale environmental changes.

[0159] In summary, this method can provide stable and reliable positioning output with better robustness, which can greatly reduce the situation of positioning failure caused by environmental changes; it can provide the reliability of laser positioning and report problems in time when the positioning error is large; the fusion of relative pose and global absolute pose can reduce the positioning error fluctuation and the maximum error.

[0160] This method can be applied in autonomous driving scenarios. Through a laser positioning framework that includes absolute positioning, relative positioning, environmental risk assessment, and position confidence assessment, it provides continuous, stable, and reliable positioning of the vehicle. This method can be applied to different types of autonomous vehicles, such as logistics vehicles, public service vehicles, medical service vehicles, and terminal service vehicles. Any vehicle equipped with a laser odometry can use this method for positioning.

[0161] For logistics vehicles, they can transport goods to appropriate locations along a set route during autonomous driving. During autonomous driving, the final absolute pose is obtained by fusing the frame-relative pose of the current point cloud frame mapped to the local map, the map-absolute pose of the local map mapped to the global map, and the frame-absolute pose of the current point cloud frame mapped to the global map. Based on the final absolute pose, abnormal global points are determined and considered when determining the frame-absolute pose of the next point cloud frame, making the frame-absolute pose determination more accurate.

[0162] Similarly, for public service vehicles, such as fire trucks, de-icing trucks, water trucks, snowplows, garbage disposal vehicles, traffic control vehicles, etc., in order to accurately determine their current location during the autonomous driving process, these public service vehicles need to accurately determine their own posture. These public service vehicles can also be configured with this method, using the current point cloud frame collected by the onboard laser odometry, the prior global map and the local map to determine the frame relative posture of the current point cloud frame mapped to the local map, the map absolute posture of the local map mapped to the global map, and the frame absolute posture of the current point cloud frame mapped to the global map. Based on the frame relative posture, the frame absolute posture and the map absolute posture, the final absolute posture is determined. Then, based on the final absolute posture and the current point cloud frame, the abnormal global point is determined, and the frame absolute posture of the next point cloud frame is determined based on the global map that identifies the abnormal global point, thereby improving the accuracy of the final absolute posture. The process for other vehicles with different functions is similar, so it will not be repeated here.

[0163] Through this embodiment, when determining the final absolute pose, the frame-relative pose and the map-absolute pose are fused into the frame-absolute pose, making the fused final absolute pose more accurate and reducing error fluctuation and maximum error. Moreover, based on the final absolute pose, the current point cloud frame can be mapped to the global map, thereby determining the degree of position change of each global point in the global map, and identifying abnormal global points caused by environmental changes. This can avoid interference from abnormal global points when determining the frame-absolute pose of the next point cloud frame, thereby avoiding positioning failures caused by environmental changes and improving robustness.

[0164] The positioning method of this embodiment can be executed by any appropriate electronic device with positioning capability, including but not limited to: a server, a mobile terminal (such as a mobile phone, a PAD, etc.) and a PC.

[0165] Example 3

[0166] Reference Figure 3 , shows a structural block diagram of the positioning device of Example 3 of the present application.

[0167] In this embodiment, the positioning device includes:

[0168] A first acquisition module 302 is configured to acquire a frame relative pose for mapping a current point cloud frame collected by a lidar into a local map, a map absolute pose for mapping the local map into the global map, and a frame absolute pose for mapping the current point cloud frame into the global map;

[0169] A fusion module 304 is configured to perform posture fusion on the frame absolute posture, the map absolute posture, and the frame relative posture to obtain a final absolute posture corresponding to the current point cloud frame;

[0170] An evaluation module 306 is configured to determine, based on the final absolute pose and the current point cloud frame, abnormal global points in the global map whose position change is greater than or equal to a screening threshold, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global points are identified.

[0171] Optionally, the first acquisition module 302 is used to determine the map predicted absolute pose for mapping the local map into the global map when acquiring the map absolute pose for mapping the local map into the global map; and match the local map with the global map based on the map predicted absolute pose to determine the map absolute pose for mapping the local map into the global map.

[0172] Optionally, the first acquisition module 302 is used to obtain the frame absolute pose corresponding to the previous point cloud frame, the second reference pose of the previous point cloud frame in the local coordinate system, and the first reference pose for mapping the local map to the local coordinate system when determining the map predicted absolute pose for mapping the local map to the global map; based on the first reference pose, the frame absolute pose corresponding to the previous point cloud frame, and the second reference pose of the previous point cloud frame, determine the map predicted absolute pose corresponding to the local map.

[0173] Optionally, the first acquisition module 302 is used to determine the frame predicted absolute pose for mapping the current point cloud frame to the global map based on the final absolute pose corresponding to the previous point cloud frame collected by the lidar, the second reference pose of the previous point cloud frame in the local coordinate system, and the second reference pose of the current point cloud frame in the local coordinate system when obtaining the frame absolute pose of the current point cloud frame mapped to the global map; determine the normal reflection points and abnormal reflection points among the reflection points contained in the current point cloud frame, wherein the abnormal reflection point is a reflection point whose first offset distance from the corresponding global point in the global map is greater than or equal to a change threshold; match the normal reflection points other than the abnormal reflection points in the current point cloud frame with the global map according to the frame predicted absolute pose, and determine the frame absolute pose corresponding to the current point cloud frame according to the matching result.

[0174] Optionally, the first acquisition module 302 is used to obtain the inter-frame displacement increment when determining the normal reflection points and abnormal reflection points among the reflection points contained in the current point cloud frame, and determine the predicted absolute pose corresponding to the current point cloud frame based on the inter-frame displacement increment and the final absolute pose corresponding to the previous point cloud frame collected by the lidar; determine the first offset distance between the reflection point of the current point cloud frame and the corresponding global point in the global map based on the predicted absolute pose and the global map corresponding to the current point cloud frame; determine and identify the abnormal reflection point in the current point cloud frame based on the first offset distance of each of the reflection points.

[0175] Optionally, the first acquisition module 302 is used to match the normal reflection points other than the abnormal reflection points in the current point cloud frame with the global map according to the frame predicted absolute pose, and determine the frame absolute pose corresponding to the current point cloud frame according to the matching result. According to the frame predicted absolute pose, each normal reflection point in the current point cloud frame is mapped to the global map to obtain a first mapping position of each normal reflection point; according to the first mapping position of each normal reflection point and the position of the global point in the global map, k adjacent global points corresponding to each normal reflection point are determined; for each normal reflection point, the abnormal global point among the k adjacent global points corresponding to the normal reflection point is screened out, and the surface offset distance of each normal reflection point relative to the reference surface constructed by the remaining normal global points is determined; according to each surface offset distance, the frame absolute pose corresponding to the current point cloud frame is determined.

[0176] Optionally, the fusion module 304 is used to obtain a first reference pose of the local map in a set local coordinate system, a second reference pose of the current point cloud frame in the local coordinate system, and a transformation relationship between the local coordinate system and the global coordinate system corresponding to the global map; construct a fused graph optimization problem according to graph optimization rules based on the frame absolute pose, the map absolute pose and the frame relative pose; solve the graph optimization problem according to the first reference pose, the second reference pose and the transformation relationship to obtain the final fused absolute pose corresponding to the current point cloud frame.

[0177] Optionally, the device further comprises:

[0178] A second acquisition module 308 is configured to map the current point cloud frame to the global map according to the final absolute pose, so as to obtain a second mapping position of each reflection point in the current point cloud frame in the global map;

[0179] a determination module 310 configured to determine a second offset distance between each reflection point and a corresponding nearest global point in the global map based on the second mapped position of each reflection point and a nearest global point corresponding to each reflection point in the global map;

[0180] The normalization module 312 is configured to determine the confidence level of the final absolute pose using a normalization function according to the second offset distance of the reflection point corresponding to each global point.

[0181] Optionally, the evaluation module 306 is used to, when determining, based on the final absolute pose and the current point cloud frame, an abnormal global point in the global map whose position change is greater than or equal to a screening threshold, map the reflection point of the current point cloud frame to the global map according to the final absolute pose to obtain a second mapping position of the reflection point in the global map; determine, based on the second mapping position of each reflection point, a second offset distance between each reflection point and the corresponding nearest global point in the global map; and determine, based on the second offset distance of the reflection point corresponding to each global point, an abnormal global point whose position change is greater than or equal to the screening threshold.

[0182] The positioning device of this embodiment is used to implement the corresponding positioning methods in the aforementioned multiple method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module in the positioning device of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0183] Example 4

[0184] Reference Figure 4 , shows a structural diagram of an electronic device according to the fourth embodiment of the present application. The specific embodiment of the present application does not limit the specific implementation of the electronic device.

[0185] like Figure 4 As shown, the electronic device may include: a processor (processor) 402 , a communication interface (Communications Interface) 404 , a memory (memory) 406 , and a communication bus 408 .

[0186] in:

[0187] The processor 402 , the communication interface 404 , and the memory 406 communicate with each other via a communication bus 408 .

[0188] The communication interface 404 is used to communicate with other electronic devices or servers.

[0189] The processor 402 is configured to execute the program 410 , and specifically may execute the relevant steps in the above positioning method embodiment.

[0190] Specifically, the program 410 may include program codes, which include computer operation instructions.

[0191] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs, or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0192] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0193] The program 410 may be specifically configured to enable the processor 402 to execute the steps corresponding to the aforementioned positioning method.

[0194] The specific implementation of each step in procedure 410 can be found in the corresponding descriptions of the corresponding steps and units in the above-mentioned positioning method embodiment, and will not be repeated here. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding process descriptions in the above-mentioned method embodiment, and will not be repeated here.

[0195] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0196] The above-described method according to the embodiment of the present application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium downloaded via a network and stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, processor or hardware, the positioning method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the positioning method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the positioning method shown herein.

[0197] Those skilled in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this application.

[0198] The above implementation methods are only used to illustrate the embodiments of the present application, and are not intended to limit the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present application, and the scope of patent protection of the embodiments of the present application should be defined by the claims.

Claims

1. A positioning method, comprising: Obtaining a frame relative pose for mapping a current point cloud frame collected by a lidar into a local map, a map absolute pose for mapping the local map into a global map, and a frame absolute pose for mapping the current point cloud frame into the global map; Performing posture fusion on the frame absolute posture, the map absolute posture, and the frame relative posture to obtain a final absolute posture corresponding to the current point cloud frame; According to the final absolute pose and the current point cloud frame, an abnormal global point in the global map whose position change degree is greater than or equal to a screening threshold is determined, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global point is identified.

2. The method according to claim 1, wherein The obtaining of an absolute map pose for mapping the local map to the global map includes: determining a map-predicted absolute pose for mapping the local map into the global map; An absolute pose is predicted based on the map, and the local map is matched with the global map to determine a map absolute pose for mapping the local map into the global map.

3. The method according to claim 2, wherein: Determining a map-predicted absolute pose for mapping the local map into the global map includes: Obtaining a frame absolute pose corresponding to a previous point cloud frame, a second reference pose of the previous point cloud frame in a local coordinate system, and a first reference pose for mapping a local map to the local coordinate system; A map-predicted absolute pose corresponding to the local map is determined based on the first reference pose, the frame absolute pose corresponding to the previous point cloud frame, and the second reference pose of the previous point cloud frame.

4. The method according to claim 1, wherein The obtaining of the absolute pose of the frame mapped to the global map by the current point cloud frame includes: Determining a frame predicted absolute pose for mapping the current point cloud frame to the global map based on a final absolute pose corresponding to a previous point cloud frame acquired by the lidar, a second reference pose of the previous point cloud frame in a local coordinate system, and a second reference pose of the current point cloud frame in the local coordinate system; Determining normal reflection points and abnormal reflection points among the reflection points included in the current point cloud frame, wherein the abnormal reflection point is a reflection point whose first offset distance from the corresponding global point in the global map is greater than or equal to a change threshold; According to the frame predicted absolute pose, normal reflection points other than abnormal reflection points in the current point cloud frame are matched with the global map, and the frame absolute pose corresponding to the current point cloud frame is determined according to the matching result.

5. The method according to claim 4, wherein The determining of normal reflection points and abnormal reflection points among the reflection points included in the current point cloud frame includes: Obtaining an inter-frame displacement increment, and determining a predicted absolute pose corresponding to the current point cloud frame based on the inter-frame displacement increment and a final absolute pose corresponding to a previous point cloud frame acquired by the lidar; Determining a first offset distance between a reflection point of the current point cloud frame and a corresponding global point in the global map based on the predicted absolute pose and a global map corresponding to the current point cloud frame; According to the first offset distance of each reflection point, abnormal reflection points in the current point cloud frame are determined and identified.

6. The method according to claim 5, wherein: Predicting the absolute pose according to the frame, matching normal reflection points other than abnormal reflection points in the current point cloud frame with the global map, and determining the frame absolute pose corresponding to the current point cloud frame according to the matching result, includes: According to the frame predicted absolute pose, each normal reflection point in the current point cloud frame is mapped to the global map to obtain a first mapping position of each normal reflection point; Determining k adjacent global points corresponding to each normal reflection point according to the first mapping position of each normal reflection point and the position of the global point in the global map; For each normal reflection point, screen out abnormal global points from the k neighboring global points corresponding to the normal reflection point, and determine a surface offset distance of each normal reflection point relative to a reference surface constructed by the remaining normal global points; Determine the absolute frame pose corresponding to the current point cloud frame based on the surface offset distances.

7. The method according to claim 1, wherein The performing posture fusion on the frame absolute posture, the map absolute posture and the frame relative posture to obtain the final absolute posture corresponding to the current point cloud frame includes: Obtaining a first reference pose of the local map in a set local coordinate system, a second reference pose of the current point cloud frame in the local coordinate system, and a conversion relationship between the local coordinate system and a global coordinate system corresponding to the global map; Constructing a fused graph optimization problem according to a graph optimization rule based on the frame absolute pose, the map absolute pose, and the frame relative pose; The graph optimization problem is solved according to the first reference pose, the second reference pose and the transformation relationship to obtain a fused final absolute pose corresponding to the current point cloud frame.

8. The method according to claim 1, wherein The method further comprises: Mapping the current point cloud frame to the global map according to the final absolute pose to obtain a second mapping position of each reflection point in the current point cloud frame in the global map; determining, based on the second mapping position of each reflection point and the nearest global point corresponding to each reflection point in the global map, a second offset distance between each reflection point and the nearest global point corresponding to the reflection point in the global map; The confidence of the final absolute pose is determined using a normalization function according to the second offset distance of the reflection point corresponding to each global point.

9. The method according to claim 1, wherein: The determining, based on the final absolute pose and the current point cloud frame, abnormal global points in the global map whose position change degree is greater than or equal to a screening threshold, includes: Mapping the reflection point of the current point cloud frame to the global map according to the final absolute pose to obtain a second mapping position of the reflection point in the global map; determining, according to the second mapped position of each reflection point, a second offset distance between each reflection point and a corresponding nearest global point in the global map; According to the second offset distances of the reflection points corresponding to the global points, abnormal global points having a position change degree greater than or equal to a screening threshold are determined.

10. A positioning device comprising: A first acquisition module is configured to acquire a frame relative pose for mapping a current point cloud frame collected by a lidar into a local map, a map absolute pose for mapping the local map into a global map, and a frame absolute pose for mapping the current point cloud frame into the global map; A fusion module, configured to perform posture fusion on the frame absolute posture, the map absolute posture, and the frame relative posture to obtain a final absolute posture corresponding to the current point cloud frame; An evaluation module is configured to determine, based on the final absolute pose and the current point cloud frame, abnormal global points in the global map whose position change is greater than or equal to a screening threshold, so as to determine the frame absolute pose of the next point cloud frame based on the global map in which the abnormal global points are identified.

11. An electronic device comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the positioning method according to any one of claims 1 to 9.

12. A computer storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the positioning method according to any one of claims 1 to 9 is implemented.

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