WO2022247137A1 - 机器人及其充电桩识别方法和装置 - Google Patents

机器人及其充电桩识别方法和装置 Download PDF

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Publication number
WO2022247137A1
WO2022247137A1 PCT/CN2021/127150 CN2021127150W WO2022247137A1 WO 2022247137 A1 WO2022247137 A1 WO 2022247137A1 CN 2021127150 W CN2021127150 W CN 2021127150W WO 2022247137 A1 WO2022247137 A1 WO 2022247137A1
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point cloud
charging pile
pose
robot
point
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PCT/CN2021/127150
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English (en)
French (fr)
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赵勇胜
弓建仁
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深圳市优必选科技股份有限公司
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Publication of WO2022247137A1 publication Critical patent/WO2022247137A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications

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  • the present application belongs to the field of robots, and in particular relates to a robot and a charging pile identification method and device thereof.
  • a common method for wheeled robots to identify charging piles is to use infrared rays to align with the out-of-line interface on the charging pile, and adjust the pose of the robot relative to the charging pile through infrared communication, so as to complete the automatic charging of the robot back to the pile. Because the efficiency of infrared alignment operation is not high, and the success rate is low.
  • the shape of the charging pile can be identified by single-line lidar to determine the pose of the charging pile.
  • this method is usually limited to charging piles with special shapes, which is not conducive to improving the scope of application of the charging pile identification method.
  • the embodiment of the present application provides a robot and its charging pile identification method and device to solve the problem in the prior art that the charging pile identification is limited to a special shape, which is not conducive to improving the scope of application of the charging pile identification method.
  • the first aspect of the embodiments of the present application provides a method for identifying a charging pile for a robot, the method comprising:
  • the point cloud segment after the pose conversion is matched with the template point cloud, and the point cloud segment corresponding to the charging pile is determined according to the matching score.
  • the point cloud data is segmented according to the predetermined size data of the surface of the charging pile to obtain multiple point cloud segments, including:
  • the two adjacent points are divided into different point cloud segments
  • the point cloud segment is split into two or more according to a predetermined fourth distance threshold.
  • the first distance threshold is the thickness of the curved portion of the front surface of the charging pile
  • the second distance threshold is the width of the curved portion of the front surface of the charging pile that is a predetermined multiple.
  • the The method before segmenting the point cloud data to obtain multiple point cloud segments, the The method also includes:
  • determining the pose of the template point cloud of the charging pile includes:
  • the pose of the template point cloud is determined according to the position of the center of mass of the template point cloud and the direction of the template point cloud towards the robot in the charging state.
  • the method further includes:
  • the point cloud template is sampled according to a predetermined point interval, or according to a predetermined distance interval, to obtain a sampled template point cloud.
  • determining the pose of the obtained point cloud segmentation includes:
  • the centroid pose of the point cloud segment is determined according to the centroid coordinates and the centroid direction of the point cloud segment.
  • the method further includes:
  • the pose of the charging pile relative to the robot center is determined.
  • the second aspect of the embodiments of the present application provides a charging pile identification device for a robot, the device comprising:
  • the point cloud data acquisition unit is used to obtain the point cloud data of the scene where the robot is located through the single-line lidar;
  • the segmentation unit is used to segment the point cloud data according to the predetermined size data of the surface of the charging pile to obtain a plurality of point cloud segments, and determine the pose of the obtained point cloud segments;
  • the pose transformation unit is used to determine the pose transformation matrix of the point cloud segmentation according to the pose of the point cloud segmentation and the predetermined pose of the template point cloud of the charging pile, and adjust the pose transformation matrix according to the pose transformation matrix.
  • the matching unit is configured to match the point cloud segment after pose conversion with the template point cloud, and determine the point cloud segment corresponding to the charging pile according to the matching score.
  • the third aspect of the embodiments of the present application provides a robot, including a memory, a processor, and a computer program stored in the memory and operable on the processor.
  • the processor executes the computer program, the following steps are implemented: The steps of any one of the methods in one aspect.
  • the fourth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of the first aspect is implemented A step of.
  • the embodiment of the present application has the beneficial effect that: after the present application acquires the point cloud data of the scene where the robot is located through the single-line laser radar, the point cloud data is segmented in combination with the size data on the surface of the charging pile, and the charging point is obtained.
  • the point cloud segment matched with the pile size information can effectively filter the interference point cloud in the scene, and then further position the point cloud segment according to the pose of the point cloud segment and the pose of the predetermined template point cloud.
  • Pose transformation, through the point cloud segment after pose transformation to match the score to determine the charging pile can further improve the matching accuracy of the charging pile and point cloud segmentation, thereby effectively improving the scope of application of the charging pile identification method.
  • FIG. 1 is a three-dimensional schematic diagram of a charging pile for a robot provided in an embodiment of the present application
  • Fig. 2 is a schematic top view of a charging pile provided by the embodiment of the present application.
  • Fig. 3 is a schematic diagram of the surface curve of the charging pile provided by the embodiment of the present application.
  • Fig. 4 is a schematic diagram of the implementation flow of a charging pile identification method for a robot provided in an embodiment of the present application;
  • Fig. 5 is a schematic diagram of charging pile parameters provided by the embodiment of the present application.
  • Fig. 6 is a schematic diagram of a charging pile identification device for a robot provided in an embodiment of the present application.
  • Fig. 7 is a schematic diagram of a robot provided by an embodiment of the present application.
  • infrared identification When current robots identify charging piles, they usually use infrared identification.
  • An infrared emitting gun and an infrared interface are respectively installed on the robot and the charging pile, and the infrared rays emitted by the infrared emitting gun are aimed at the infrared interface on the charging pile, and the posture of the robot relative to the charging pile is adjusted through infrared communication. Since the infrared signal strength is affected by many factors, the recharging speed is slow and the accuracy is not high.
  • single-line laser radar can be used to scan, and the charging piles can be set in a specific shape, such as a shape that is less similar to life scenes, including circular arc shapes, etc. . Due to the limitation of the special shape, the style of the charging pile that the robot can recognize is limited, which is not conducive to improving the application range of the charging pile identification method.
  • the embodiment of the present application proposes a charging pile identification method for a robot, which uses the size data on the surface of the charging pile to segment the point cloud data acquired by the single-line lidar to obtain point cloud segments. Then determine the pose transformation matrix according to the pose of the point cloud segment and the pose of the template point cloud, perform pose transformation on the point cloud segment through the pose transformation matrix, and perform the pose transformation according to the transformed point cloud segment and the template point cloud Matching determines the point cloud corresponding to the charging pile according to the matching score, so that the charging pile does not have to be limited to a specific shape, and the application range of the charging pile identification method is improved.
  • FIG. 1 is a three-dimensional schematic diagram of a charging pile for a robot provided in an embodiment of the present application.
  • the robot can scan through the single-line laser radar, intersect with the front surface of the charging pile on the horizontal plane corresponding to the height of the single-line laser radar, and obtain the surface curve of the front surface of the charging pile, that is, Figure 1 part shown in bold.
  • Fig. 2 is a schematic top view of the charging pile provided by the embodiment of the present application. As shown in Figure 2, the surface curve of the charging pile scanned by the robot's single-line lidar includes the width information of the charging pile.
  • the template point cloud can be established in advance by the robot.
  • the robot can start from the first point in the upper left corner to record its coordinate position according to the point cloud data of the surface curve read, and use this point to gradually search for the points in the nearest point cloud data. Points, record the coordinate positions of the searched points one by one. Until there is no other point in the preset range around the last point except the known point, the search ends. For example, if there are 5 pixels around the last point and there are no other points except known points, the search ends, and the recorded point set is called a template point cloud.
  • the coordinates of the center of mass can be expressed as M0(M0.x, M0.y), and the normal vector of the center of mass can be
  • the charging direction for the alignment robot that is, the downward direction of the alignment is the direction of the center of mass, which is ⁇ /2.
  • the generated template point cloud in order to improve the calculation efficiency, can be sampled, so that the matching calculation can be performed by sampling the remaining points to improve the matching calculation efficiency.
  • Sampling can be performed by the number of interval points or by a predetermined interval distance. For example, you can keep a point every N points and remove the rest, or you can keep a point every 1-2 cm.
  • Fig. 4 is a flow chart of the implementation of a charging pile identification method for a robot provided in the embodiment of the present application, which is described in detail as follows:
  • the point cloud data of the scene where the robot is located is acquired through a single-line lidar.
  • the robot in the embodiment of the present application can, when recharging, follow the pre-set location area where the charging pile is located, and after the robot enters the location area, use the single-line laser radar to perform spotting according to the predetermined collection cycle. Scanning and collection of cloud data.
  • the single-line lidar refers to a radar whose line beam emitted by a laser source is a single line, including simple lidar for triangulation ranging and TOF simple lidar.
  • Single-line lidar has fast scanning speed, strong resolution, high reliability, and faster response in terms of angular frequency and sensitivity, and is more accurate in ranging distance and accuracy of obstacles.
  • the point cloud data is segmented according to the predetermined size data of the surface of the charging pile to obtain a plurality of point cloud segments, and the poses of the obtained point cloud segments are determined.
  • a preliminary filtering operation can also be performed on the point cloud data.
  • the filtering operation may filter out points corresponding to points in the point cloud data whose distance is greater than the fifth distance threshold by comparing the distance between the robot and the point with a preset fifth distance threshold.
  • the fifth distance threshold may be any value within a range of 1.2 meters to 3.5 meters. When the distance between the point in the detected point cloud data and the robot is greater than the fifth distance threshold, it indicates that the point is far away from the robot and may temporarily not participate in the segmentation operation.
  • the fifth distance threshold may be related to a preset location area.
  • the fifth distance threshold may be correspondingly smaller, and when the range of the set location area is larger, the fifth distance threshold may be correspondingly larger.
  • the size data of the surface of the charging pile may include the thickness d1 of the curved portion on the front surface of the charging pile, and the width d2 of the curved portion on the front surface of the charging pile.
  • the curved part of the front surface can be the curved part that can be scanned by the robot laser radar in the front and the front of the side.
  • d1 may be the distance from the front surface to the rear surface that can be scanned by the robot lidar in front of and on the side of the charging pile.
  • segmenting the point cloud data can include:
  • the distance between two adjacent points in the point cloud data is greater than the first distance threshold, for example, it may be greater than the thickness of the charging pile at the single-line lidar scanning point, then these two adjacent points can be divided into different point cloud points. part.
  • the distance between the Nth point and the N+1th point is x, and x>d1, then the Nth point and the N+1th point are divided into two point cloud segments.
  • the point cloud can be segmented Discarding, that is, filtering out point clouds that do not meet the width of the charging pile.
  • the point cloud segment can be further segmented, Get two or more point cloud segments.
  • the third distance threshold may be a predetermined multiple of the second distance threshold, and the predetermined multiple may be 1.2-2 times.
  • point clouds with a width of 1.2 times d2 from the first point on the left to the right can be extracted respectively. And from the first point on the right to the left, the point cloud with a width of 1.2 times d2 is used as two new point cloud segments, and the original point cloud segment larger than 1.5 times is removed.
  • the divided point cloud segments can be more accurately matched and calculated with the template point cloud.
  • the point cloud segment After determining the point cloud segment corresponding to the point cloud data collected by the robot, the point cloud segment can be matched with the preset template point cloud.
  • the pose transformation can be performed according to the centroid pose of the template point cloud of the charging pile and the centroid pose of the point cloud segment, so that The pose of the point cloud segmentation is matched with the pose of the template point cloud.
  • the pose of the template point cloud and point cloud segmentation can be represented by the pose of the centroid of the template point cloud, or the coordinates of the first point and the last point of the template point cloud and point cloud segmentation
  • the coarse pose Pc of point cloud segmentation is denoted as (x2, y2, th).
  • the pose transformation matrix H1 transformed from the point cloud segment pose to the template point cloud pose can be determined, so that The pose of the transformed point cloud segment matches the pose of the template point cloud.
  • the point cloud segment after the pose conversion is matched with the template point cloud, and the point cloud segment corresponding to the charging pile is determined according to the matching score.
  • ICP International full name is Iterative Closest Point
  • Chinese full name is Iterative Closest Point Algorithm
  • NDT Choinese full name is Normal Distribution Transformation
  • English full name is Normal Distribution Transformation
  • the full name is Normal Distribution Transform) point cloud matching algorithm, which matches each point of the point cloud segment with the points of the template point cloud to obtain the transformation matrix H2 and the corresponding matching score S.
  • the score can be the average distance of ICP matching, or the cost function result of NDT matching. According to the size of the matching score, select the point cloud segment with the largest matching score, which is the point cloud segment corresponding to the charging pile.
  • the application obtains the point cloud data of the scene where the robot is located through a single-line laser radar
  • the point cloud data is segmented in combination with the size data on the surface of the charging pile, and the point cloud segmentation matching the size information of the charging pile is obtained, so that the scene can be effectively filtered
  • the pose transformation is performed on the point cloud segment, and the matching score is obtained through the pose transformed point cloud segment
  • Determining the charging pile can further improve the matching accuracy of the charging pile and point cloud segmentation, thereby effectively improving the scope of application of the charging pile identification method.
  • Fig. 6 is a schematic diagram of a charging pile identification device for a robot provided in an embodiment of the present application. As shown in Fig. 6, the device includes:
  • the point cloud data acquisition unit 601 is used to obtain the point cloud data of the scene where the robot is located through a single-line laser radar;
  • the segmentation unit 602 is configured to segment the point cloud data according to the predetermined size data of the surface of the charging pile, obtain a plurality of point cloud segments, and determine the pose of the obtained point cloud segments;
  • the pose transformation unit 603 is configured to determine the pose transformation matrix of the point cloud segmentation according to the pose of the point cloud segment and the predetermined pose of the template point cloud of the charging pile, and adjust the pose transformation matrix according to the pose transformation matrix. Describe the pose of the point cloud segment;
  • the matching unit 604 is configured to match the point cloud segment after the pose conversion with the template point cloud, and determine the point cloud segment corresponding to the charging pile according to the matching score.
  • the charging pile identification device for the robot shown in FIG. 6 corresponds to the charging pile identification method for the robot shown in FIG. 4 .
  • Fig. 7 is a schematic diagram of a robot provided by an embodiment of the present application.
  • the robot 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and operable on the processor 70, such as a charging pile recognition program of the robot .
  • the processor 70 executes the computer program 72, the steps in the embodiments of the above-mentioned charging pile identification method for each robot are realized.
  • the processor 70 executes the computer program 72, the functions of the modules/units in the above-mentioned device embodiments are implemented.
  • the computer program 72 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 71 and executed by the processor 70 to complete this application.
  • the one or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 72 in the robot 7 .
  • the robot may include, but not limited to, a processor 70 and a memory 71 .
  • FIG. 7 is only an example of the robot 7, and does not constitute a limitation to the robot 7. It may include more or less components than shown in the illustration, or combine certain components, or different components, such as
  • the robot may also include input and output devices, network access devices, buses, and the like.
  • the so-called processor 70 can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the memory 71 may be an internal storage unit of the robot 7 , such as a hard disk or memory of the robot 7 . Described memory 71 also can be the external storage device of described robot 7, for example the plug-in type hard disk that is equipped on described robot 7, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, Flash card (Flash Card), etc. Further, the memory 71 may also include both an internal storage unit of the robot 7 and an external storage device. The memory 71 is used to store the computer program and other programs and data required by the robot. The memory 71 can also be used to temporarily store data that has been output or will be output.
  • the disclosed apparatus/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.
  • the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments in this application can also be completed by hardware related to computer program instructions.
  • the computer program can be stored in a computer-readable storage medium.
  • the computer program When executed by a processor, the steps in the above-mentioned various method embodiments can be realized.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer-readable media Excluding electrical carrier signals and telecommunication signals.

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Abstract

一种机器人及其充电桩识别方法、装置、机器人和计算机可读存储介质，该方法包括：通过单线激光雷达获取机器人所在场景的点云数据（S401）；根据预定的充电桩表面的尺寸数据，对点云数据进行分段，得到多个点云分段并确定其位姿（S402）；根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定位姿变换矩阵，根据位姿变换矩阵调整点云分段的位姿（S403）；将转换位姿后的点云分段与模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段（S404）。该方法能够有效的提高充电桩与点云分段的匹配精度，提升充电桩识别方法的适用范围。

Description

机器人及其充电桩识别方法和装置
本申请要求于2021年05月28日在中国专利局提交的、申请号为202110595202.5的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于机器人领域，尤其涉及机器人及其充电桩识别方法和装置。
背景技术
随着科学技术的发展，智能的机器人为人们的生活工作提供了越来越多的便利。其中，如何使机器人能够高效稳定的完成智能回充，是目前所研究的机器人的热点问题。
常见的轮式机器人识别充电桩的方法是使用红外线对准充电桩上的线外接口，通过红外通信的方式，调整机器人相对于充电桩的位姿，从而完成机器人自动回桩充电。由于红外线对准操的效率不高，且成功率较低。为了提高充电桩识别效率，可以通过单线激光雷达识别充电桩的形状来确定充电桩位姿，但这种方式通常局限于特殊形状的充电桩，不利于提升充电桩识别方法的适用范围。
技术问题
有鉴于此，本申请实施例提供了一种机器人及其充电桩识别方法和装置，以解决现有技术中充电桩识别时局限于特殊形状，不利于提升充电桩识别方法的适用范围的问题。
技术解决方案
本申请实施例的第一方面提供了一种机器人的充电桩识别方法，所述方法包括：
通过单线激光雷达获取机器人所在场景的点云数据；
根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿；
根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿；
将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
结合第一方面，在第一方面的第一种可能实现方式中，根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，包括：
根据点云数据中的两个相邻的点之间的距离大于第一距离阈值，则将这两个相邻的点划分为不同的点云分段；
如果所述点云分段的第一个点和最后一个点之间的距离小于第三距离阈值，则丢弃该点云分段；
如果所述点云分段的第一个点和最后一个点之间的距离大于第二距离阈值，则按照预定的第四距离阈值将所述点云分段拆分为两个或两个以上的点云分段；
其中，第一距离阈值为充电桩前表面曲线部分的厚度，所述第二距离阈值为预定倍数的充电桩前表面曲线部分的宽度。
结合第一方面或第一方面的第一种可能实现方式，在第一方面的第二种可能实现方式中，在对所述点云数据进行分段，得到多个点云分段之前，所述方法还包括：
将点云数据中的点与机器人之间的距离与预设的第五距离阈值进行比较，过滤掉点云数据对应的距离大于所述第五距离阈值的点。
结合第一方面，在第一方面的第三种可能实现方式中，确定充电桩的模板点云的位姿，包括：
通过单线激光雷达从正面获取充电桩的表面曲线；
从左上角第一个点开始搜索，逐个记录表面曲线上的点的坐标，直到除了已知点外预定范围内没有点，则结束记录，得到模板点云；
根据模板点云的质心位置和模板点云朝向处于充电状态的机器人的方向，确定所述模板点云的位姿。
结合第一方面的第三种可能实现方式，在第一方面的第四种可能实现方式中，在得到模板点云之后，所述方法还包括：
按照预定的点数间隔，或者按照预定的距离间隔，对所述点云模板进行抽样，得到抽样后的模板点云。
结合第一方面，在第一方面的第五种可能实现方式中，确定所得到的点云分段的位姿，包括：
根据点云分段的第一个点和最后一个点的坐标，确定点云分段的质心坐标；
根据点云分段的第一个点和最后一个点的连线的法向量确定所述点云分段的质心方向；
根据所述点云分段的质心坐标和质心方向确定所述点云分段的质心位姿。
结合第一方面，在第一方面的第六种可能实现方式中，在确定充电桩所对应的点云分段之后，所述方法还包括：
根据所述点云分段的位姿变换矩阵，以及激光雷达相对于机器人中心位置的关系矩阵，确定充电桩相对于机器人中心的位姿。
本申请实施例的第二方面提供了一种机器人的充电桩识别装置，所述装置包括：
点云数据获取单元，用于通过单线激光雷达获取机器人所在场景的点云数据；
分段单元，用于根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿；
位姿变换单元，用于根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿；
匹配单元，用于将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
本申请实施例的第三方面提供了机器人，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如第一方面任一项所述方法的步骤。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面任一项所述方法的步骤。
有益效果
本申请实施例与现有技术相比存在的有益效果是：本申请通过单线激光雷达获取机器人所在场景的点云数据后，结合充电桩表面的尺寸数据对点云数据进行分段，得到与充电桩尺寸信息匹配的点云分段，从而可以有效的过滤场景中的干扰点云，然后再进一步根据点云分段的位姿和预先确定的模板点云的位姿对点云分段进行位姿变换，通过位姿变换后的点云分段进行匹配得分确定充电桩，能够进一步提高充电桩与点云分段的匹配精度，从而能够有效的提升充电桩识别方法的适用范围。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种机器人的充电桩的三维示意图；
图2是本申请实施例提供的一种充电桩俯视示意图；
图3是本申请实施例提供的充电桩的表面曲线示意图；
图4是本申请实施例提供的一种机器人的充电桩识别方法的实现流程示意图；
图5为本申请实施例提供的充电桩参数示意图；
图6是本申请实施例提供的一种机器人的充电桩识别装置的示意图；
图7是本申请实施例提供的机器人的示意图。
本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
目前的机器人进行充电桩识别时，通常采用红外识别的方式。在机器人和充电桩上分别设置红外线发射枪和红外线接口，红外发射枪所发射的红外线对准充电桩上的红外接口，通过红外通信的方式调整机器人相对于充电桩的位姿。由于红外信号强度受多种因素的影响，导致回充速度较慢，并且精度也不高。
为了提高充电桩回充效率和成功率，可以采用单线激光雷达进行扫描的方式，将充电桩设置为特定的形状，比如设置为与生活场景中相似形状较少的形状，包括如圆弧形状等。由于特殊形状的限定，导致机器人所能识别的充电桩的样式受限，不利于提升充电桩识别方法的应用范围。
基于此，本申请实施例提出了一种机器人的充电桩识别方法，通过充电桩表面的尺寸数据，对单线激光雷达所获取的点云数据分割得到点云分段。然后根据点云分段的位姿与模板点云的位姿确定位姿变换矩阵，通过位姿变换矩阵对点云分段进行位姿变换，根据变换后的点云分段与模板点云进行匹配，根据匹配的得分确定充电桩对应的点云，从而使得充电桩不必局限于特定形状，提升充电桩识别方法的应用范围。
图1为本申请实施例提供的一种机器人的充电桩的三维示意图。如图1所示，充电桩安装固定后，机器人可以通过单线激光雷达扫描，在单线激光雷达的高度对应的水平面，与充电桩前面表相交，得到充电桩前表面部分的表面曲线，即图1所示的粗线部分。
图2为本申请实施例提供的充电桩俯视示意图。如图2所示，充电桩被机器人的单线激光雷达所扫描得到的表面曲线，包括充电桩的宽度信息等。
在进行充电桩识别时，可以预先由机器人建立模板点云。如图3所示，可以由机器人根据所读取的表面曲线的点云数据，从左上角第一个点开始，记录其坐标位置，并以该点逐步进行搜索周围最近的点云数据中的点，逐个记录所搜索到的点的坐标位置。直到最后一个点周围的预设范围内除了已知点外，不存在其它点，则搜索结束。比如，最后一个点周围5个像素点，除了已知的点，不存在其它点，则结束搜索，所记录下来的点集称为模板点云。
对于图3所示的由机器人根据所读取的表面曲线的点云数据所记录的充电桩的表面曲线示意图，其质心坐标可以表示为M0(M0.x，M0.y)，质心法向量可以为对准机器人充电的方向，即对准向下的方向为质心方向，即为π/2。模板点云的质心的位姿可以表示为：Pm＝(M0.x，M0.y，-π/2)。与图3所示的充电桩的表面曲线对应的充电位置的机器人中心的位姿为P＝(Px，Py，Pth)，且Pth通常为π/2，机器人的方向通常为机器人朝向充电桩的方向。
在本申请实施例中，为了提高计算效率，可以对所生成的模板点云进行抽样处理，从 而通过抽样所剩下的点进行匹配计算，提高匹配计算效率。抽样可以根据间隔点的数量进行抽样，也可以根据预定的间隔距离进行抽样。比如，可以每隔N个点保留一个点，剩下的去掉，或者也可以每隔1-2厘米保留一个点。
图4为本申请实施例提供的一种机器人的充电桩识别方法的实现流程图，详述如下：
在S401中，通过单线激光雷达获取机器人所在场景的点云数据。
具体的，本申请实施例中的机器人，可以在进行回充时，按照预先设定的充电桩所在的位置区域，在机器人进入该位置区域后，按照预定的采集周期，通过单线激光雷达进行点云数据的扫描和采集。
所述单线激光雷达，是指激光源发出的线束是单线的雷达，包括三角测距的单纯激光雷达及TOF单纯激光雷达。单线激光雷达具有扫描速度快、分辨率强、可靠性高，并且在角频率及灵敏度上反应更快捷，在障碍物的测距距离和精度上更为精准。
在S402中，根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿。
在对点云数据进行支点分段的划分之前，还可以对点云数据进行初步的过滤操作。该过滤操作可以通过机器人与点之间的距离与预设的第五距离阈值进行比较的方式，过滤掉点云数据中的点对应的距离大于所述第五距离阈值的点。
其中，第五距离阈值可以为1.2米-3.5米范围内的任意值。当检测到的点云数据中的点与机器人的距离大于第五距离阈值，则表明该点与机器人的距离较远，可以暂时不参与分段操作。
其中，第五距离阈值可以与预设的位置区域相关。当所设定的位置区域的范围越小，第五距离阈值可以相应的取较小值，当所设定的位置区域的范围较大，则第五距离阈值可以相应的取较大值。
其中，充电桩表面的尺寸数据，如图5所示，可以包括充电桩前表面曲线部分的厚度d1，充电桩前表面曲线部分的宽度d2。其中，前表面曲线部分可以为机器人激光雷达在前面，以及侧前面可以扫描到的曲线部分。d1可以为机器人激光雷达在充电桩前面和侧前面可扫描到的前表面到后表面的距离。
根据充电桩表面的尺寸数据，对点云数据进行分段可以包括：
如果点云数据中的相邻两个点的距离大于第一距离阈值，比如可以大于充电桩在单线激光雷达扫描处的厚度，则可以将这两个相邻的点划分至不同的点云分段。比如第N点与第N+1点之间的距离为x，且x>d1，则将第N点与第N+1点划分至两个点云分段中。
如果点云数据中所划分的点云分段中，其第一个点至最后一个点之间的距离小于预定的第二距离阈值，比如小于充电桩的宽度，则可以将该点云分段舍弃，即对不符合充电桩宽度的点云分段筛选过滤掉。
另外，如果得到的点云分段的第一个点至最后一个点之间的距离大于第二距离阈值，并且大于预定的第三距离阈值，则可以对该点云分段进一步进行分割处理，得到两个或两个以上的点云分段。比如，第三距离阈值可以为预定倍数的第二距离阈值，该预定倍数可以为1.2倍-2倍。
比如，当检测到点云分段的第一个点至最后一个点之间的距离大于1.5倍d2，则可以分别提取从左侧第一个点往右，宽度为1.2倍d2的点云，以及从右侧第一个点往左，宽度为1.2倍d2的点云，作为两个新的点云分段，并去掉原来大于1.5倍的点云分段。通过点云分段的划分，使得所划分的点云分段能够更为准确的与模板点云进行匹配和计算。
在S403中，根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿。
在确定机器人所采集的点云数据对应的点云分段后，可以将点云分段与预设的模板点云进行匹配。为了提升点云分段与模板点云的匹配准确度，减少误匹配操作，可以根据充电桩的模板点云的质心位姿，与点云分段的质心位姿，进行位姿变换，从而使得点云分段 的位姿与模板点云的位姿匹配。
其中，模板点云和点云分段的位姿，可以通过模板点云的质心的位姿来表示，或者也可以通过模板点云和点云分段的第一个点和最后一个点的坐标来确定质心坐标，通过该点的法向量且对准充电位置的方向为质心方向。如果点云分段的第一个点的坐标为(x0，y0)，(x1，y1)，那么质心坐标(x2，y2)＝((x0+x1)/2,(y0+y1)/2)。点云分段的方向th为：th＝atan[(y1-y0)/(x1-x0)]-π/2。点云分段的粗略位姿Pc表示为(x2，y2，th)。
根据所计算得到的点云分段的位姿，结合所确定的模板点云的位姿，可以确定由点云分段位姿，变换为模板点云位姿的位姿变换矩阵H1，从而使变换后的点云分段的位姿，与模板点云的位姿匹配。
在S404中，将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
将位姿变换后的点云分段，与模板点云进行匹配时，可以通过ICP(英文全称为Iterative Closest Point，中文全称为迭代最近点算法)或NDT(中文全称为正态分布变换，英文全称为Normal Distribution Transform)点云匹配算法，把点云分段的每个点与模板点云的点进行匹配，得到转换矩阵H2，以及对应的匹配分值S。该分值可以为ICP匹配的平均距离，也可以为NDT匹配的代价函数结果。根据匹配分值的大小，选择匹配分值最大的点云分段，即为充电桩所对应的点云分段。
在确定充电桩所对应的点云分段后，假设单线激光雷达相对于机器人中心的位置关系矩阵为H3，那么，充电桩相对于机器人中心的位姿Pd可以表示为：Pd＝P*(H2^-1)*H3。
本申请通过单线激光雷达获取机器人所在场景的点云数据后，结合充电桩表面的尺寸数据对点云数据进行分段，得到与充电桩尺寸信息匹配的点云分段，从而可以有效的过滤场景中的干扰点云，然后再进一步根据点云分段的位姿和预先确定的模板点云的位姿对点云分段进行位姿变换，通过位姿变换后的点云分段进行匹配得分确定充电桩，能够进一步提高充电桩与点云分段的匹配精度，从而能够有效的提升充电桩识别方法的适用范围。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
图6为本申请实施例提供的一种机器人的充电桩识别装置的示意图，如图6所示，该装置包括：
点云数据获取单元601，用于通过单线激光雷达获取机器人所在场景的点云数据；
分段单元602，用于根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿；
位姿变换单元603，用于根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿；
匹配单元604，用于将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
图6所示的机器人的充电桩识别装置，与图4所示的机器人的充电桩识别方法对应。
图7是本申请一实施例提供的机器人的示意图。如图7所示，该实施例的机器人7包括：处理器70、存储器71以及存储在所述存储器71中并可在所述处理器70上运行的计算机程序72，例如机器人的充电桩识别程序。所述处理器70执行所述计算机程序72时实现上述各个机器人的充电桩识别方法实施例中的步骤。或者，所述处理器70执行所述计算机程序72时实现上述各装置实施例中各模块/单元的功能。
示例性的，所述计算机程序72可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器71中，并由所述处理器70执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序72在所述机器人7中的执行过程。
所述机器人可包括，但不仅限于，处理器70、存储器71。本领域技术人员可以理解， 图7仅仅是机器人7的示例，并不构成对机器人7的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述机器人还可以包括输入输出设备、网络接入设备、总线等。
所称处理器70可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器71可以是所述机器人7的内部存储单元，例如机器人7的硬盘或内存。所述存储器71也可以是所述机器人7的外部存储设备，例如所述机器人7上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器71还可以既包括所述机器人7的内部存储单元也包括外部存储设备。所述存储器71用于存储所述计算机程序以及所述机器人所需的其他程序和数据。所述存储器71还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序指令相关的硬件来完成，所述的计算机 程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (10)

  1. 一种机器人的充电桩识别方法，其特征在于，所述方法包括：
    通过单线激光雷达获取机器人所在场景的点云数据；
    根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿；
    根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿；
    将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
  2. 根据权利要求1所述的方法，其特征在于，根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，包括：
    根据点云数据中的两个相邻的点之间的距离大于第一距离阈值，则将这两个相邻的点划分为不同的点云分段；
    如果所述点云分段的第一个点和最后一个点之间的距离小于第三距离阈值，则丢弃该点云分段；
    如果所述点云分段的第一个点和最后一个点之间的距离大于第二距离阈值，则按照预定的第四距离阈值将所述点云分段拆分为两个或两个以上的点云分段；
    其中，第一距离阈值为充电桩前表面曲线部分的厚度，所述第二距离阈值为预定倍数的充电桩前表面曲线部分的宽度。
  3. 根据权利要求1或2所述的方法，其特征在于，在对所述点云数据进行分段，得到多个点云分段之前，所述方法还包括：
    将点云数据中的点与机器人之间的距离与预设的第五距离阈值进行比较，过滤掉点云数据对应的距离大于所述第五距离阈值的点。
  4. 根据权利要求1所述的方法，其特征在于，确定充电桩的模板点云的位姿，包括：
    通过单线激光雷达从正面获取充电桩的表面曲线；
    从左上角第一个点开始搜索，逐个记录表面曲线上的点的坐标，直到除了已知点外预定范围内没有点，则结束记录，得到模板点云；
    根据模板点云的质心位置和模板点云朝向处于充电状态的机器人的方向，确定所述模板点云的位姿。
  5. 根据权利要求4所述的方法，其特征在于，在得到模板点云之后，所述方法还包括：
    按照预定的点数间隔，或者按照预定的距离间隔，对所述点云模板进行抽样，得到抽样后的模板点云。
  6. 根据权利要求1所述的方法，其特征在于，确定所得到的点云分段的位姿，包括：
    根据点云分段的第一个点和最后一个点的坐标，确定点云分段的质心坐标；
    根据点云分段的第一个点和最后一个点的连线的法向量确定所述点云分段的质心方向；
    根据所述点云分段的质心坐标和质心方向确定所述点云分段的质心位姿。
  7. 根据权利要求1所述的方法，其特征在于，在确定充电桩所对应的点云分段之后，所述方法还包括：
    根据所述点云分段的位姿变换矩阵，以及激光雷达相对于机器人中心位置的关系矩阵，确定充电桩相对于机器人中心的位姿。
  8. 一种机器人的充电桩识别装置，其特征在于，所述装置包括：
    点云数据获取单元，用于通过单线激光雷达获取机器人所在场景的点云数据；
    分段单元，用于根据预定的充电桩表面的尺寸数据，对所述点云数据进行分段，得到多个点云分段，确定所得到的点云分段的位姿；
    位姿变换单元，用于根据点云分段的位姿和预先确定的充电桩的模板点云的位姿，确 定点云分段的位姿变换矩阵，根据所述位姿变换矩阵调整所述点云分段的位姿；
    匹配单元，用于将转换位姿后的点云分段与所述模板点云进行匹配，根据匹配的得分确定充电桩所对应的点云分段。
  9. 一种机器人，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至7任一项所述方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述方法的步骤。
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