CN114911232A - Pose determination method and device for mobile robot - Google Patents
Pose determination method and device for mobile robot Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The embodiment of the application provides a pose determination method and a pose determination device for a mobile robot. The method is used for improving the positioning accuracy of the mobile robot. The mobile robot includes a lidar, the method comprising: in the process of executing tasks by the mobile robot, if the current environment condition does not meet the preset condition, detecting the reflective strips in the current environment; when at least one light reflecting strip is detected, acquiring the position coordinates of the at least one light reflecting strip and first point cloud data which are acquired by the laser radar and used for representing the characteristics of an obstacle; projecting the position coordinates and the first point cloud data to a pre-constructed grid map, and determining a first residual error between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual error between the first point cloud data and second point cloud data contained in a matching grid map; determining a first pose of the mobile robot based on the first residual and the second residual.
Description
Technical Field
The invention relates to the technical field of mobile robot positioning, in particular to a pose determination method and a pose determination device of a mobile robot.
Background
When a mobile robot performs a task operation, such as object handling in a warehouse, the mobile robot needs to accurately know the pose of the mobile robot in the current environment in the environment, so that the mobile robot can accurately perform the task. At present, a mobile robot is generally positioned by using a laser radar, that is, the mobile robot detects a reflected signal in an observation scene by installing the laser radar, determines the characteristics of an obstacle, and positions according to the determined characteristics of the obstacle.
However, in some scenarios, the included obstacle features are less, or the storage environment changes (for example, the original environment changes due to cargo handling, and workers frequently move), which may affect the reflected signal detected by the lidar, resulting in lower positioning accuracy for positioning based on the reflected signal in the lidar detection observation environment.
Disclosure of Invention
The embodiment of the application provides a pose determining method and device of a mobile robot. The method is used for improving the positioning accuracy of the mobile robot.
In a first aspect, a pose determination method for a mobile robot including a lidar includes:
in the process of executing tasks by the mobile robot, if the current environment condition does not meet the preset condition, detecting the reflective strips in the current environment;
when at least one light reflecting strip is detected, acquiring the position coordinates of the at least one light reflecting strip and first point cloud data which are acquired by the laser radar and used for representing the characteristics of an obstacle;
projecting the position coordinates and the first point cloud data to a pre-constructed grid map, and determining a first residual error between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual error between the first point cloud data and second point cloud data contained in a matching grid map;
determining a first pose of the mobile robot based on the first residual and the second residual.
Optionally, the mobile robot further comprises an odometer, and the method further comprises:
determining a second pose of the mobile robot based on the pose of the mobile robot at the previous moment and the variation of the rotation angle and the movement distance of the mobile robot estimated by the odometer; the pose at the previous moment is the pose recorded by the mobile robot at the last time;
determining a target pose of the mobile robot based on the first pose and the second pose.
Optionally, the determining the target pose of the mobile robot based on the first pose and the second pose includes:
acquiring a first weight corresponding to the first posture and a second weight corresponding to the second posture;
and performing pose fusion on the first pose and the second pose based on the first weight and the second weight to obtain the target pose.
Optionally, the method further includes:
determining the number of the light reflecting strips;
if the number of the light reflecting strips is 1, projecting the point cloud data acquired by the laser radar to the grid map based on the first pose, determining the first pose score, and if the first pose score is greater than a first preset threshold value, determining that the first pose is a reliable pose; the grid map comprises the probability of the point cloud falling on each grid, the probability is used for representing the score of the point cloud falling on each grid, and the reliable pose refers to the pose with the matching degree with the actual pose higher than a threshold value;
and if the number of the light reflecting strips is greater than or equal to 2, determining that the first pose is a reliable pose.
Optionally, the detecting the reflective strip in the current environment includes:
determining the distance between adjacent point clouds in the point cloud data acquired by the laser radar, and clustering the adjacent point clouds of which the distances are smaller than a second preset threshold value to form a plurality of point cloud clusters;
determining whether a first point cloud cluster exists in the plurality of point cloud clusters; wherein point clouds with energy intensity greater than a preset intensity threshold exist in the first point cloud cluster;
and if so, determining that the reflective strip is detected in the current environment.
Optionally, the determining whether a first point cloud cluster exists in the plurality of point cloud clusters includes:
determining the number of point clouds contained in each of the plurality of point cloud clusters;
acquiring a second point cloud cluster with the point cloud number larger than a third preset threshold;
determining whether the first point cloud cluster is present in the second point cloud cluster.
Optionally, the method further includes:
determining a normal vector of the first point cloud cluster;
judging whether the included angle between the normal vector and the ground is smaller than a fourth preset threshold value or not, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip;
and/or the presence of a gas in the gas,
determining the distance between any two point clouds in the first point cloud cluster;
and judging whether the distance between any two point clouds is smaller than a fifth preset threshold value, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip.
In a second aspect, there is provided a pose determination apparatus of a mobile robot including a laser radar, the apparatus including:
the detection module is used for detecting the light reflecting strips in the current environment when the current environment condition does not meet the preset condition in the process of executing the task by the mobile robot;
the processing module is used for acquiring the position coordinates of at least one reflective strip and first point cloud data which are acquired by the laser radar and used for representing the characteristics of an obstacle when the at least one reflective strip is detected;
the processing module is further configured to project the position coordinates and the first point cloud data to a pre-constructed grid map, and determine a first residual between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual between the first point cloud data and second point cloud data included in a matching grid map;
the processing module is further configured to determine a first pose of the mobile robot based on the first residual and the second residual.
Optionally, the mobile robot further includes a odometer, and the processing module is further configured to:
determining a second pose of the mobile robot based on the pose of the mobile robot at the previous moment and the variation of the rotation angle and the movement distance of the mobile robot estimated by the odometer; the pose at the previous moment is the pose recorded by the mobile robot at the last time;
determining a target pose of the mobile robot based on the first pose and the second pose.
Optionally, the processing module is specifically configured to:
acquiring a first weight corresponding to the first posture and a second weight corresponding to the second posture;
and performing pose fusion on the first pose and the second pose based on the first weight and the second weight to obtain the target pose.
Optionally, the processing module is further configured to:
determining the number of the light reflecting strips;
if the number of the light reflecting strips is 1, projecting the point cloud data acquired by the laser radar to the grid map based on the first pose, determining the score of the first pose, and if the score of the first pose is greater than a first preset threshold, determining that the first pose is a reliable pose; the grid map comprises the probability of the point cloud falling on each grid, the probability is used for representing the score of the point cloud falling on each grid, and the reliable pose refers to the pose with the matching degree with the actual pose higher than a threshold value;
and if the number of the light reflecting strips is greater than or equal to 2, determining that the first pose is a reliable pose.
Optionally, the detection module is specifically configured to:
determining the distance between adjacent point clouds in the point cloud data acquired by the laser radar, and clustering the adjacent point clouds of which the distances are smaller than a second preset threshold value to form a plurality of point cloud clusters;
determining whether a first point cloud cluster exists in the plurality of point cloud clusters; wherein point clouds with energy intensity greater than a preset intensity threshold exist in the first point cloud cluster;
and if so, determining that the reflective strip is detected in the current environment.
Optionally, the detection module is specifically configured to:
determining the number of point clouds contained in each of the plurality of point cloud clusters;
acquiring a second point cloud cluster with the point cloud number larger than a third preset threshold;
determining whether the first point cloud cluster is present in the second point cloud cluster.
Optionally, the processing module is further configured to:
determining a normal vector of the first point cloud cluster;
judging whether an included angle between the normal vector and the ground is smaller than a fourth preset threshold value or not, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip;
and/or the presence of a gas in the gas,
determining the distance between any two point clouds in the first point cloud cluster;
and judging whether the distance between any two point clouds is smaller than a fifth preset threshold value, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip.
In a third aspect, an electronic device is provided, which includes:
a memory for storing program instructions;
a processor for calling the program instructions stored in the memory and executing the steps comprised in any of the methods of the first aspect according to the obtained program instructions.
In a fourth aspect, there is provided a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the steps included in the method of any one of the first aspects.
In a fifth aspect, a computer program product containing instructions is provided, which when run on a computer causes the computer to execute the pose determination method of the mobile robot described in the above various possible implementations.
In the embodiment of the application, in the process of executing a task, if the current environment condition does not meet a preset condition, a reflective strip in the current environment is detected, when at least one reflective strip is detected, the position coordinate of the at least one reflective strip and first point cloud data, obtained by a laser radar, for representing the characteristics of an obstacle are obtained, the position coordinate of the reflective strip and the first point cloud data are projected to a pre-constructed grid map, a first residual error between the position coordinate of the reflective strip and the position coordinate of the reflective strip calibrated in the grid map and a second residual error between the first point cloud data and second point cloud data contained in a matched grid map are determined, and a first pose of the mobile robot is determined based on the first residual error and the second residual error.
That is to say, according to the method and the device, the point cloud data and the position coordinates of the light reflecting strips acquired by the laser radar are combined with the matching results of the point cloud data and the position coordinates of the calibrated light reflecting strips contained in the pre-constructed grid map, so that even if the point cloud data acquired by the laser radar is influenced by the environment in the process of determining the pose of the mobile robot, the positioning accuracy which is finally obtained is higher than the positioning accuracy which is obtained only through the laser radar for positioning because the position coordinates of the light reflecting strips are required to be referred and are fixed, and the positioning accuracy of the mobile robot is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application.
Fig. 1 is a schematic structural diagram of a mobile robot according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating an arrangement of reflective strips under varying environments according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating an arrangement of light-reflecting strips in a smooth long corridor environment according to an embodiment of the present disclosure;
fig. 4 is a flowchart of a pose determination method for a mobile robot according to an embodiment of the present disclosure;
fig. 5 is a block diagram of a pose determination apparatus for a mobile robot according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. In the present application, the embodiments and features of the embodiments may be arbitrarily combined with each other without conflict. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
The terms "first" and "second" in the description and claims of the present application and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the term "comprises" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. The "plurality" in the present application may mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.
Before describing the embodiments of the present application, some technical features of the present application will be described to facilitate understanding for those skilled in the art.
And (4) milemeter: preliminarily estimating the angle and distance variation of the mobile robot, so that the pose of the robot at the current moment can be estimated according to the pose variation of the previous moment and the pose of the robot at the previous moment, wherein the robot is a common wheeled encoder;
laser radar: the sensor is used for acquiring two-dimensional plane information and detecting two-dimensional plane contour information of the surrounding environment;
a light reflecting strip: and the material has strong laser reflectivity. When the laser scans the reflective strips, the point cloud data received by the sensor has very strong reflectivity, and the reflection intensity can be obviously distinguished from surrounding materials.
The idea of the present application is presented below.
As described above, when the storage environment changes or the obstacle characteristics are small, the positioning accuracy of positioning based on the reflection signal in the lidar detection observation environment is low. In the related art, in order to solve the technical problem, one scheme is to perform hybrid navigation based on a laser radar and a reflector, wherein an initial pose of a robot is initialized, the pose of the robot is preliminarily estimated based on a value of a odometer, a map is built through laser point cloud data acquired by the current pose, and the position of the reflector is calibrated in the built map in a manual calibration mode; when the robot moves, the acquired laser point cloud data is judged, whether the acquired point cloud data at least comprises point cloud data of 3 reflectors is determined, if not, the current laser point cloud data is compared with map data, the map data matched with the point cloud data is searched for to estimate the pose of the robot, if yes, 3 reflector data are extracted, the pose of the robot is estimated by a triangulation method, and an original map is corrected according to the pose determined by the reflectors, wherein the extracted 3 reflectors are large in distance and large in separation angle, and the difference of the extracted 3 reflector data is large enough. When the storage environment changes or the included barrier features are few (namely the environmental conditions do not meet the preset conditions), if 3 reflectors are not detected, the pose is determined through laser point cloud, the robot positioning accuracy is low at the moment, if more than 3 reflectors are detected, the pose of the robot is determined through a triangulation method, and the two poses have large difference in calculation accuracy, so that the robot can obviously twist and unsmooth in the moving process. And for laser radar, when detecting the reflector, can all detect the edge of reflector as the reflector, detection error is big.
In view of this, an embodiment of the present application provides a pose determination method for a mobile robot, where in a process of a mobile robot executing a task, if it is determined that a current environment condition does not satisfy a preset condition, a light-reflecting strip in the current environment is detected, when the at least one light-reflecting strip is detected, a position coordinate of the at least one light-reflecting strip and first point cloud data, which are obtained by a laser radar and used for characterizing an obstacle, are obtained, the position coordinate of the light-reflecting strip and the first point cloud data are projected onto a pre-constructed grid map, a first residual between the position coordinate of the light-reflecting strip and a position coordinate of the light-reflecting strip calibrated in the grid map and a second residual between the first point cloud data and second point cloud data included in a matching grid map are determined, and a first pose of the mobile robot is determined based on the first residual and the second residual. Therefore, the mode of combining the light reflecting strips and the first point cloud data representing the characteristics of the obstacles can avoid the mobile robot from obviously twisting in the moving process, and the positioning accuracy is higher than that of the simple point cloud data or light reflecting plate data using the laser radar for positioning.
After introducing the design concept of the embodiment of the present application, some simple descriptions are provided below for application scenarios to which the technical solution of the embodiment of the present application can be applied, and it should be noted that the application scenarios described below are only used for describing the embodiment of the present application and are not limited. In specific implementation, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.
In the embodiment of the present application, the pose determination method and apparatus for a mobile robot may be applied to a mobile robot, please refer to fig. 1, where fig. 1 is a schematic structural diagram of a mobile robot provided in the embodiment of the present application, the mobile robot provided in the embodiment of the present application includes a mobile chassis, a laser radar, and a camera, and a motion controller, a motor, a battery, an embedded computer, a odometer, and the like are disposed in the mobile chassis. The mobile robot is at the in-process of carrying out the task, carry out abundant scanning to the storage environment through laser radar, obtain the reflection of light strip and goods shelves of demarcating among the storage environment, the workstation, the house support frame, barrier information such as the goods that need carry, wherein, this application is in order to compromise precision and the stability of laser detection reflection of light strip, cut into width 2cm with the reflection of light strip, rectangular about 20cm long, guarantee that the mobile robot is at the removal in-process, at different distances, even the pitch angle that different equipment has different laser also can detect the reflection of light strip, and consider that the workstation in the current environment, goods etc. are mobilizable, consequently in order to ensure the reflection of light strip after arranging, the position of reflection of light strip can not change, consequently generally arrange the reflection of light strip at the house support frame (or also can be called as the wall). And in the arrangement process of the light reflecting strips, the distance between every two adjacent light reflecting strips is at least kept to be 30cm or more, and the distances between the light reflecting strips cannot be equidistant, so that the mismatching caused by the formation of similar characteristics is avoided, and meanwhile, the middle of the light reflecting strips is arranged at a position close to the height of a laser radar of the mobile robot. Referring to fig. 2 and 3, fig. 2 is a schematic diagram illustrating an arrangement of the reflective strips under a changing environment according to an embodiment of the present disclosure, and fig. 3 is a schematic diagram illustrating an arrangement of the reflective strips under a smooth corridor according to an embodiment of the present disclosure.
In this application embodiment, tailor into width 2cm with reflection of light strip, the use cost of reflection of light strip can be practiced thrift to rectangular about 20cm long, and effectively avoids being the condition of reflection of light strip with the edge detection to promote the accuracy that reflection of light strip detected.
The following describes a pose determination method of a mobile robot according to an embodiment of the present application with reference to drawings in the specification. Referring to fig. 4, a process of determining the pose of the mobile robot in the embodiment of the present application is described as follows:
step 401: in the process of executing tasks by the mobile robot, if the current environment condition does not meet the preset condition, detecting the reflective strips in the current environment;
in the embodiment of the present application, the mobile robot may detect the current environmental condition in real time during the process of executing the task, and determine whether the current environmental condition meets the preset condition, for example, if the current environmental profile is good and is not obviously occluded, the current environment condition is considered to meet the preset condition, the pose of the mobile robot can be determined directly on the basis of the point cloud data representing the obstacle features acquired by the laser radar, if the current environment has workers who walk, or the environment is changed due to the fact that goods are being carried, or the environment is currently in a smooth long corridor environment, the current environment is considered not to satisfy the preset condition, and at this time, in order to avoid the data acquired by the laser radar being affected by the current environment, the reflective strips in the current environment need to be detected, therefore, the pose of the mobile robot can be determined based on the position coordinates of the light reflecting strips and data acquired by the laser radar.
As a possible implementation manner, considering that point clouds corresponding to the same object are in an aggregation state, the method and the device can traverse point cloud data acquired by the laser radar, determine whether the distance between adjacent point clouds is smaller than a second preset threshold, if so, consider that the adjacent point clouds are continuous and belong to point cloud data corresponding to the same obstacle plane, and then aggregate the point clouds belonging to the same obstacle plane into one type to form a point cloud cluster, wherein the current environment comprises a plurality of obstacles, so that after traversing all the point cloud data acquired by the laser radar, the point cloud data can be clustered to form a plurality of point cloud clusters. Judging whether point clouds with the intensity larger than a preset intensity threshold value exist in each point cloud cluster, if point clouds with the intensity larger than the preset threshold value exist in a first point cloud cluster in the plurality of point cloud clusters, determining that the first point cloud cluster comprises point cloud data corresponding to the light reflecting strips (namely determining that the light reflecting strips are arranged on the obstacle plane outline corresponding to the first point cloud cluster), and indicating that the light reflecting strips are detected in the current environment.
For example, traversing point cloud data acquired by a laser radar, calculating a distance between two adjacent point clouds (for example, a first point cloud and a second point cloud), if the distance between the first point cloud and the second point cloud is smaller than a second preset threshold (for example, 8cm), regarding the first point cloud and the second point cloud as point clouds corresponding to the same obstacle plane contour (for example, the same wall or the same cargo), clustering the first point cloud and the second point cloud to form a point cloud cluster (for example, point cloud cluster 1), then continuously calculating the distance between the first point cloud or the second point cloud and other adjacent point clouds, if the distance between the third point cloud and the second point cloud is smaller than 8cm, clustering the third point cloud to point cluster 1, and so on, obtaining a plurality of point cloud clusters corresponding to a plurality of obstacle plane contours, and after traversing all the point cloud data to obtain a plurality of point cloud clusters, and judging whether point clouds with the intensity larger than a preset intensity threshold exist in each point cloud cluster, and if yes, indicating that the light reflecting strips are detected in the current environment. It should be noted that the preset intensity threshold is related to the type of the lidar, and the preset intensity thresholds corresponding to different types of lidar are different, for example, the preset intensity threshold corresponding to the north-ocean lidar is 10000, and the preset intensity threshold corresponding to the sine lidar is 1000.
In order to reduce the computation amount in the pose determination process, in the process of detecting the light-reflecting strips, discrete point cloud clusters (for example, point cloud clusters containing a point cloud quantity smaller than a certain quantity) can be removed first, that is, the number of point clouds contained in each of a plurality of point cloud clusters formed by clustering is judged first, a second point cloud cluster with a point cloud quantity larger than a third pre-shooting threshold value is obtained, and then whether a first point cloud cluster with a point cloud intensity larger than a preset intensity threshold value exists in the second point cloud cluster or not is judged.
In one possible embodiment, whether the retroreflective strips determined in the foregoing process are accurate or not can also be determined in the following manner.
In a first possible implementation manner, a normal vector of the first point cloud cluster is determined, whether an included angle between the normal vector and the ground is smaller than a fourth preset threshold value is judged, and if the included angle is smaller than the fourth preset threshold value, the first point cloud cluster is determined to be a point cloud cluster corresponding to the light reflecting strip. The light reflecting strips are arranged on the wall surface, so that the light reflecting strips are perpendicular to the ground, namely, the normal vectors of the light reflecting strips are parallel to the ground, and whether the accuracy of the light reflecting strips determined in the process is determined by judging whether the included angle between the normal vector of the first point cloud cluster and the ground is smaller than a fourth preset threshold value or not.
In a second possible implementation manner, the distance between any two point clouds in the first point cloud cluster is determined, whether the distance between any two point clouds is smaller than a fifth preset threshold value is judged, and if the distance between any two point clouds is smaller than the fifth preset threshold value, the first point cloud cluster is determined to be the point cloud cluster corresponding to the light reflecting strip. Because the size of the light reflecting strip is fixed (namely the length and the width are fixed), whether the light reflecting strip determined in the process is accurate can be determined by whether the distance between the point clouds in the point cloud cluster is greater than the length of the light reflecting strip.
The first possible embodiment and the second possible embodiment may be used individually or in combination, and when the first possible embodiment and the second possible embodiment are used in combination, the accuracy of detecting the light-reflecting strips is determined to be higher.
Step 402: when at least one light reflecting strip is detected, acquiring the position coordinate of the at least one light reflecting strip and first point cloud data which are acquired by a laser radar and used for representing the characteristics of an obstacle;
in the embodiment of the application, when at least one light reflecting strip is detected, the point cloud cluster corresponding to the light reflecting strip is averaged to obtain the center coordinate of the light reflecting strip, and the center coordinate is used as the position coordinate of the light reflecting strip for subsequent operation.
Step 403: projecting the position coordinates and the first point cloud data to a pre-constructed grid map, and determining a first residual error between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual error between the first point cloud data and second point cloud data contained in the matching grid map;
as a possible implementation manner, a grid map needs to be constructed in advance, and in the process of constructing the grid map, the reflective strips in the current environment need to be detected, and the positions of the detected reflective strips are calibrated in the grid map.
In the embodiment of the application, the detected position coordinates of the light-reflecting strip (for example, the light-reflecting strip 1) and the first point cloud data are projected to a pre-constructed grid map, the position coordinates of the light-reflecting strip 1 and the first point cloud data are respectively matched with the position coordinates of the light-reflecting strip calibrated in the grid map and the point cloud data contained in the grid map, the light-reflecting strip 2 closest to the light-reflecting strip 1 in the grid map is determined, a first residual error between the position coordinates of the light-reflecting strip 1 and the position coordinates of the light-reflecting strip 2 is calculated, and a second residual error between the first point cloud data and second point cloud data contained in the matched grid map is calculated. In a possible implementation manner, after the first point cloud data is projected to the pre-constructed raster map, whether the weight of the point cloud is greater than 0 or not can be checked, if the weight of the point cloud is greater than 0, the point cloud is considered to be a long-term feature, the first point cloud data is matched with the pre-constructed raster map, and if the weight of the point cloud is not greater than 0, the point cloud is considered to be a short-term feature, and the point cloud is not matched with the pre-constructed raster map.
In other embodiments, in the process of constructing the grid map, after the detected light-reflecting strips are calibrated in the grid map, coordinates of the light-reflecting strips can be read to construct a KDtree, in the process of executing a task by the mobile robot, the position coordinates corresponding to the currently detected at least one light-reflecting strip are sequentially traversed, at least one global light-reflecting strip which can be matched with the position coordinates corresponding to the currently detected at least one light-reflecting strip is found from the KDtree of the light-reflecting strips of the grid map, the currently detected light-reflecting strip and the corresponding global light-reflecting strip are recorded as a light-reflecting strip pair, and a first residual error corresponding to each light-reflecting strip pair is determined.
Step 404: and determining a first pose of the mobile robot based on the first residual error and the second residual error.
In the embodiment of the application, after the first residual and the second residual are obtained through calculation in the foregoing steps, weights may be further set for the first residual and the second residual, the sum of the first residual and the second residual is minimized as much as possible by adjusting the weights corresponding to the first residual and the second residual (that is, minimizing a residual term), and then the first pose of the mobile robot is determined by using a nonlinear optimization method.
In a possible implementation, after determining the first pose of the mobile robot, it may further determine whether the first pose is a reliable pose, for example, first determine the number of detected light-reflecting bars, if the number of detected light-reflecting bars is greater than or equal to 2, then determine that the first pose is a reliable pose, if the number of detected light-reflecting bars is 1, project point cloud data acquired by the laser radar onto a grid map based on the first pose, determine a probability that the point cloud falls on each grid, determine a score of the first pose based on the probability that the point cloud falls on each grid, and if the score of the first pose is greater than a first preset threshold, then determine that the first pose is a reliable pose. And the reliable pose is the pose matched with the actual pose by the height threshold.
In a specific implementation process, when only one reflective strip is detected, the pose of the mobile robot can be accurately determined based on the reflective strip assisted laser radar, and the reflective strip is used for assisted positioning instead of simple positioning mode switching, so that the positioning result is smoother. Meanwhile, the width of the light reflecting strips is only about 2cm, and errors of light reflecting strip detection are much smaller than those of the light reflecting plates, so that the accuracy of pose estimation of the mobile robot is effectively improved.
The above embodiment provides a process of moving a first pose of a robot, and the following embodiment provides a way of optimizing the first pose.
In the process of executing tasks by the mobile robot, the current pose is recorded at intervals, in the embodiment of the application, the pose and the movement and odometry data (including the rotation angle and the variation of the movement distance of the mobile robot) recorded at the previous moment (last recorded) of the mobile robot are obtained, the second pose of the mobile robot is determined based on the pose and the odometry data at the previous moment, and determining the target pose of the mobile robot based on the first pose and the second pose, specifically, acquiring a first weight corresponding to the first pose and a second weight corresponding to the second pose, performing pose fusion on the first pose and the second pose based on the first weight and the second weight to obtain the target pose, for example, fusion pose estimation is performed using the graph optimization model, and the fusion estimated target pose is taken as the pose of the final mobile robot.
In a specific implementation process, after the first pose and the second pose are fused according to the corresponding weights, the accuracy of the finally determined pose can be higher.
In some other embodiments, the light-reflecting strip provided in the embodiments of the present application may also be used for assisting relocation, for example, when the mobile robot is just started, an initial pose of the mobile robot needs to be determined, at this time, if it is determined that the current environment contour is good and is not significantly occluded (meets a preset condition), the laser point cloud contour detected by the laser radar may be directly matched with a grid map constructed in advance, so as to confirm the initial pose of the mobile robot, and if it is determined that the current environment does not meet the preset condition, the initial pose of the mobile robot may be assisted by the detected light-reflecting strip, which includes the following specific methods:
the calibrated reflective strips in the pre-constructed grid map are loaded to the current map (such as the reflective strip map to be constructed), the origin of the pre-constructed grid map is taken as the origin of the current map, the central coordinates of the light reflecting strips are converted to the grid coordinates corresponding to the current map, then the position centers of the light reflecting strips are used for probability expansion, namely, the probability value of the grid where the position of the light reflecting strip is positioned is considered to be the highest, and when the position of the light reflecting strip is spread to the periphery by taking the point as the center, the probability is gradually reduced (the closer the grid probability to the center of the light-reflecting strip is, the lower the grid probability to the center of the light-reflecting strip is), a light-reflecting strip probability map is generated, the two-way sliding window is utilized to perform down-sampling on the light-reflecting strip probability map, for example, the resolution of the generated light-reflecting strip probability map is 5cm, and (4) performing down-sampling on the reflective strip probability map, and reducing 5 layers to obtain a reflective strip probability map with 5 resolution ratios. And the grid probability of the resolution map after the down-sampling is the maximum value of all grids in the sliding window space corresponding to the original resolution map. Namely, the mobile robot pose score in the resolution map based on the down-sampled resolution is guaranteed to be the upper bound of the mobile robot pose score in the original resolution map.
Before the mobile robot is shut down, the current pose is recorded every time the mobile robot normally runs for a certain distance (such as 1m) or rotates for a certain angle (such as 10 degrees), when relocation is carried out, the latest pose recorded by the mobile robot is used as an initial value of relocation, a search range (such as 4x4m) is given and used for limiting the range of pose search in the relocation process, at least three light reflecting strips are detected at the current position and used as light reflecting strip point clouds, and the optimal pose of the mobile robot is confirmed by a branch positioning method based on the light reflecting strip point clouds and a light reflecting strip probability map.
Based on the same inventive concept, the embodiment of the application provides a pose determining device of a mobile robot, and the pose determining device of the mobile robot can realize the corresponding function of the pose determining method of the mobile robot. The pose determination device of the mobile robot may be a hardware structure, a software module, or a hardware structure plus a software module. The pose determination device of the mobile robot can be realized by a chip system, and the chip system can be formed by a chip and can also comprise the chip and other discrete devices. Referring to fig. 5, the pose determination apparatus of the mobile robot includes a detection module 501 and a processing module 502. Wherein:
the detection module 501 is configured to detect a light-reflecting strip in a current environment when a current environment condition does not meet a preset condition in a process of executing a task by the mobile robot;
the processing module 502 is configured to, when at least one light-reflecting strip is detected, acquire a position coordinate of the at least one light-reflecting strip and first point cloud data, which is acquired by the laser radar and used for characterizing an obstacle, of the laser radar;
the processing module 502 is further configured to project the position coordinate and the first point cloud data to a pre-constructed grid map, and determine a first residual between the position coordinate and a position coordinate of a reflective strip calibrated in the grid map and a second residual between the first point cloud data and second point cloud data included in a matching grid map;
the processing module 502 is further configured to determine a first pose of the mobile robot based on the first residual and the second residual.
Optionally, the mobile robot further includes an odometer, and the processing module 502 is further configured to:
determining a second pose of the mobile robot based on the pose of the mobile robot at the previous moment and the variation of the rotation angle and the movement distance of the mobile robot estimated by the odometer; the pose at the previous moment is the pose recorded by the mobile robot at the last time;
determining a target pose of the mobile robot based on the first pose and the second pose.
Optionally, the processing module 502 is specifically configured to:
acquiring a first weight corresponding to the first posture and a second weight corresponding to the second posture;
and performing pose fusion on the first pose and the second pose based on the first weight and the second weight to obtain the target pose.
Optionally, the processing module 502 is further configured to:
determining the number of the light reflecting strips;
if the number of the light reflecting strips is 1, projecting the point cloud data acquired by the laser radar to the grid map based on the first pose, determining the first pose score, and if the first pose score is greater than a first preset threshold value, determining that the first pose is a reliable pose; the grid map comprises the probability of the point cloud falling on each grid, the probability is used for representing the score of the point cloud falling on each grid, and the reliable pose refers to the pose with the matching degree with the actual pose higher than a threshold value;
and if the number of the light reflecting strips is greater than or equal to 2, determining that the first pose is a reliable pose.
Optionally, the detecting module 501 is specifically configured to:
determining the distance between adjacent point clouds in the point cloud data acquired by the laser radar, and clustering the adjacent point clouds of which the distances are smaller than a second preset threshold value to form a plurality of point cloud clusters;
determining whether a first point cloud cluster exists in the plurality of point cloud clusters; wherein point clouds with energy intensity greater than a preset intensity threshold exist in the first point cloud cluster;
and if so, determining that the reflective strip is detected in the current environment.
Optionally, the detecting module 501 is specifically configured to:
determining the number of point clouds contained in each of the plurality of point cloud clusters;
acquiring a second point cloud cluster with the point cloud number larger than a third preset threshold;
determining whether the first point cloud cluster exists in the second point cloud cluster.
Optionally, the processing module 502 is further configured to:
determining a normal vector of the first point cloud cluster;
judging whether an included angle between the normal vector and the ground is smaller than a fourth preset threshold value or not, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip;
and/or the presence of a gas in the gas,
determining the distance between any two point clouds in the first point cloud cluster;
and judging whether the distance between any two point clouds is smaller than a fifth preset threshold value, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip.
All relevant contents of each step related to the aforementioned embodiment of the pose determining method of the mobile robot may be cited in the description of the function module corresponding to the pose determining apparatus of the mobile robot in the embodiment of the present application, and are not described herein again.
The division of the modules in the embodiments of the present application is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present application may be integrated in one processor, may also exist alone physically, or may also be integrated in one module by two or more modules. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
Based on the same inventive concept, the embodiment of the application provides electronic equipment. Referring to fig. 6, the electronic device includes at least one processor 601 and a memory 602 connected to the at least one processor, in this embodiment, a specific connection medium between the processor 601 and the memory 602 is not limited in this application, in fig. 6, the processor 601 and the memory 602 are connected by a bus 600 as an example, the bus 600 is represented by a thick line in fig. 6, and a connection manner between other components is only schematically illustrated and is not limited. The bus 600 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 6 for ease of illustration, but does not represent only one bus or type of bus.
In the embodiment of the present application, the memory 602 stores instructions executable by the at least one processor 601, and the at least one processor 601 may execute the steps included in the aforementioned pose determination method for a mobile robot by executing the instructions stored in the memory 602.
The processor 601 is a control center of the electronic device, and may connect various parts of the whole electronic device by using various interfaces and lines, and perform various functions and process data of the electronic device by operating or executing instructions stored in the memory 602 and calling data stored in the memory 602, thereby performing overall monitoring on the electronic device. Alternatively, processor 601 may include one or more processing units, and processor 601 may integrate an application processor, which mainly handles operating systems and application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601. In some embodiments, the processor 601 and the memory 602 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
The processor 601 may be a general-purpose processor, such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like, that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the pose determination method of the mobile robot disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
The memory 602, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory 602 may include at least one type of storage medium, and may include, for example, a flash Memory, a hard disk, a multimedia card, a card-type Memory, a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Programmable Read Only Memory (PROM), a Read Only Memory (ROM), a charge Erasable Programmable Read Only Memory (EEPROM), a magnetic Memory, a magnetic disk, an optical disk, and so on. The memory 602 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 602 in the embodiments of the present application may also be circuitry or any other device capable of performing a storage function for storing program instructions and/or data.
By programming the processor 601, the code corresponding to the pose determination method for the mobile robot described in the foregoing embodiment may be solidified into a chip, so that the chip can execute the steps of the pose determination method for the mobile robot when running.
Based on the same inventive concept, embodiments of the present application further provide a computer readable storage medium, which stores computer instructions that, when executed on a computer, cause the computer to execute the steps of the pose determination method of a mobile robot as described above.
In some possible embodiments, the aspects of the pose determination method of a mobile robot provided by the present application can also be implemented in the form of a program product comprising program code for causing a detection apparatus to perform the steps of the pose determination method of a mobile robot according to various exemplary embodiments of the present application described above in this specification when the program product is run on an electronic device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A pose determination method of a mobile robot, the mobile robot including a lidar, the method comprising:
in the process of executing tasks by the mobile robot, if the current environment condition does not meet the preset condition, detecting the reflective strips in the current environment;
when at least one light reflecting strip is detected, acquiring the position coordinates of the at least one light reflecting strip and first point cloud data which are acquired by the laser radar and used for representing the characteristics of an obstacle;
projecting the position coordinates and the first point cloud data to a pre-constructed grid map, and determining a first residual error between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual error between the first point cloud data and second point cloud data contained in a matching grid map;
determining a first pose of the mobile robot based on the first residual and the second residual.
2. The method of claim 1, wherein the mobile robot further comprises an odometer, the method further comprising:
determining a second pose of the mobile robot based on the pose of the mobile robot at the previous moment and the variation of the rotation angle and the movement distance of the mobile robot estimated by the odometer; the pose at the previous moment is the pose recorded by the mobile robot at the last time;
determining a target pose of the mobile robot based on the first pose and the second pose.
3. The method of claim 2, wherein the determining the target pose of the mobile robot based on the first pose and the second pose comprises:
acquiring a first weight corresponding to the first posture and a second weight corresponding to the second posture;
and performing pose fusion on the first pose and the second pose based on the first weight and the second weight to obtain the target pose.
4. The method of claim 1, wherein the method further comprises:
determining the number of the reflective strips;
if the number of the light reflecting strips is 1, projecting the point cloud data acquired by the laser radar to the grid map based on the first pose, determining the first pose score, and if the first pose score is greater than a first preset threshold value, determining that the first pose is a reliable pose; the grid map comprises the probability of the point cloud falling on each grid, the probability is used for representing the score of the point cloud falling on each grid, and the reliable pose refers to the pose with the matching degree with the actual pose higher than a threshold value;
and if the number of the light reflecting strips is greater than or equal to 2, determining that the first pose is a reliable pose.
5. The method of claim 1, wherein said detecting retro-reflective stripes in a current environment comprises:
determining the distance between adjacent point clouds in the point cloud data acquired by the laser radar, and clustering the adjacent point clouds of which the distances are smaller than a second preset threshold value to form a plurality of point cloud clusters;
determining whether a first point cloud cluster exists in the plurality of point cloud clusters; wherein point clouds with energy intensity greater than a preset intensity threshold exist in the first point cloud cluster;
and if so, determining that the reflective strip is detected in the current environment.
6. The method of claim 5, wherein the determining whether a first point cloud cluster exists among the plurality of point cloud clusters comprises:
determining the number of point clouds contained in each of the plurality of point cloud clusters;
acquiring a second point cloud cluster with the point cloud number larger than a third preset threshold;
determining whether the first point cloud cluster is present in the second point cloud cluster.
7. The method of claim 5, wherein the method further comprises:
determining a normal vector of the first point cloud cluster;
judging whether an included angle between the normal vector and the ground is smaller than a fourth preset threshold value or not, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip;
and/or the presence of a gas in the gas,
determining the distance between any two point clouds in the first point cloud cluster;
and judging whether the distance between any two point clouds is smaller than a fifth preset threshold value, and if so, determining that the first point cloud cluster is the point cloud cluster corresponding to the light reflecting strip.
8. A pose determination apparatus of a mobile robot, the mobile robot including a laser radar, the apparatus comprising:
the detection module is used for detecting the light reflecting strips in the current environment when the current environment condition does not meet the preset condition in the process of executing the task by the mobile robot;
the processing module is used for acquiring the position coordinates of at least one reflective strip and first point cloud data which are acquired by the laser radar and used for representing the characteristics of an obstacle when the at least one reflective strip is detected;
the processing module is further configured to project the position coordinates and the first point cloud data to a pre-constructed grid map, and determine a first residual between the position coordinates and the position coordinates of the reflective strips calibrated in the grid map and a second residual between the first point cloud data and second point cloud data included in a matching grid map;
the processing module is further configured to determine a first pose of the mobile robot based on the first residual and the second residual.
9. An electronic device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory and for executing the steps comprised by the method of any one of claims 1 to 7 in accordance with the obtained program instructions.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1-7.
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