WO2023019573A1 - 测距方法、波形检测方法、装置及相关设备 - Google Patents
测距方法、波形检测方法、装置及相关设备 Download PDFInfo
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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
- G01S7/483—Details of pulse systems
- G01S7/486—Receivers
- G01S7/487—Extracting wanted echo signals, e.g. pulse detection
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- Embodiments of the present invention relate to the technical field of laser radar, and specifically relate to a waveform detection method, a ranging method, a waveform detection device, a laser radar, an automatic driving device, and a computer-readable storage medium.
- LiDAR is a device that makes measurements by emitting laser light. Due to the existence of interference in the working environment of the laser radar, such as the distance absorption caused by the interference of multiple reflections of light in the inner cavity under harsh natural conditions or at close range, there are a large amount of interference data in the echoes received by the laser radar , the echoes generated by these situations have different amplitudes, which will cause misjudgments in lidar perception or decision-making.
- an embodiment of the present invention provides a waveform detection method for solving the technical problem in the prior art that abnormal waveforms cannot be accurately identified.
- a waveform detection method comprising:
- An abnormal waveform in the echo waveform data is determined based on the comparison result.
- the comparing the similarity between the echo waveform data and the standard waveform data to obtain the comparison result includes: performing correlation calculation between the echo waveform data and the standard waveform data to obtain A calculation result; determining the comparison result according to the calculation result.
- the correlation calculation between the echo waveform data and the standard waveform data to obtain the calculation result includes: standard amplitude dispersion according to the standard waveform data, the standard waveform The standard energy value of the data, the amplitude dispersion of the echo waveform data and the energy value of the echo waveform data are correlated to obtain a calculation result.
- the standard waveform data includes multiple sets of normal echo waveforms with different peak amplitudes, and each set of normal echo waveforms includes the peak point of the normal echo waveform and the normal echo waveform K points before and after the peak point of the waveform; the correlation calculation between the echo waveform data and the standard waveform data is carried out to obtain the calculation result, including: determining each waveform sampling point in the echo waveform data; The waveform sampling point and the K points before and after the waveform sampling point are used as a group of echo waveforms to be compared; the correlation calculation is performed on the echo waveform to be compared and the normal echo waveforms in each group to obtain calculation results.
- the correlation calculation between the echo waveform to be compared and each group of normal echo waveforms is performed, and before the calculation result is obtained, the method includes: calculating the normal echo waveform The amplitude mean value of the normal echo waveform; the amplitude mean value of the normal echo waveform is subtracted from the amplitude mean value of the normal echo waveform to obtain the standard amplitude deviation corresponding to the normal echo waveform; according to the standard amplitude deviation The difference calculates the standard energy value of the normal echo waveform.
- the correlation calculation between the echo waveform to be compared and each group of normal echo waveforms to obtain the calculation result includes: calculating the amplitude of the echo waveform to be compared mean value; subtracting the amplitude mean value from the current amplitude value of the echo waveform to be compared to obtain the current amplitude deviation corresponding to the echo waveform to be compared; calculating the pending amplitude deviation according to the current amplitude deviation Compare the current energy value of the echo waveform.
- performing correlation calculation on the echo waveform to be compared and each group of normal echo waveforms to obtain a calculation result includes: determining the multiple The target normal echo waveform corresponding to the current amplitude among the normal echo waveforms with different peak amplitudes; according to the current amplitude deviation, the current energy value, and the standard amplitude corresponding to the target normal echo waveform The value dispersion and the standard energy value corresponding to the target normal echo waveform are correlated to obtain the calculation result.
- the current amplitude deviation, the current energy value, the standard amplitude deviation corresponding to the target normal echo waveform, and the target normal echo waveform corresponding performing correlation calculations on standard energy values to obtain calculation results, including: obtaining an energy correlation result according to the standard energy value corresponding to the current energy value and the target normal echo waveform; according to the current amplitude deviation and the The standard amplitude deviation corresponding to the normal echo waveform of the target is used to obtain an amplitude correlation result; and a correlation coefficient is obtained by calculating according to the energy correlation result and the amplitude correlation result.
- a ranging method including:
- a waveform detection device including:
- An acquisition module configured to acquire echo waveform data
- a calculation module configured to compare the similarity between the echo waveform data and the standard waveform data to obtain a comparison result
- a discrimination module configured to determine the abnormal waveform in the echo waveform data according to the comparison result.
- a laser radar is provided, and the laser radar includes the waveform detection device.
- an automatic driving device including a driving device body and the lidar, and the laser radar is installed on the driving device body.
- a computer-readable storage medium where computer instructions are stored on the computer-readable storage medium, and when the computer instructions are executed by a processor, the waveform detection method is implemented.
- the embodiment of the present invention obtains the echo waveform data, and compares the echo waveform data with the preset standard waveform data to obtain the comparison result, and finally determines the abnormal waveform in the echo waveform data according to the comparison result, which can accurately It can accurately identify the normal echoes and abnormal echoes received by the lidar.
- multiple sets of echo waveforms to be compared are obtained, and correlation calculation is performed with multiple sets of normal echo waveforms with different peak amplitudes, which further improves the accuracy and efficiency of abnormal waveform identification.
- FIG. 1 shows a schematic diagram of an application environment of a waveform detection method provided by an embodiment of the present invention
- FIG. 2 shows a schematic flowchart of a waveform detection method provided by an embodiment of the present invention
- Fig. 3 shows a schematic flowchart of a ranging method provided by another embodiment of the present invention.
- Fig. 4 shows a schematic structural diagram of a waveform detection device provided by an embodiment of the present invention.
- the waveform detection method provided by the embodiment of the present invention is applied to a laser radar, and specifically may be executed by a control and processing unit of the laser radar.
- the photoelectric sensor of the lidar emits laser beams at different vertical angles in the air, and at the same time, the photoelectric sensor receives the echo returned by the target and converts it into a weak electrical signal. Number transformation, calculate the distance of the target in the digital domain.
- the control and processing unit of the laser radar is used to control the system to work according to a certain transmission and reception sequence, and at the same time process the received echo data to obtain the distance result of the target.
- the transmission unit is usually a plurality of semiconductor laser arrays, driven by voltage according to The laser is emitted at a certain time sequence. When there is a target in the emission direction, the returned echo passes through the receiving lens to the receiving photoelectric sensor, and is converted into an electrical signal. It is amplified and converted into a digital quantity in the receiving unit, and the distance result is formed after subsequent digital processing.
- the interference echo When the interference echo can pass the threshold, It will be regarded as a normal echo by the lidar, thereby forming a false point cloud, which will cause misjudgment in perception or decision-making.
- the beam scans sequentially, because the beam has a certain angular distribution in the horizontal direction, there is a transition transient state when passing the two objects, and gradually transitions from one distance to another distance, forming a transition point Clouds are called drag points between objects, and the interference caused by these drag points will also cause LiDAR to make judgment errors during the ranging process.
- the light is reflected multiple times in the machine cavity, and these redundant optical components are useless interference components. They are very close to the real target echoes at close range, and even connected together. It also affects the detection of real normal echoes.
- an embodiment of the present invention provides a waveform detection method. As shown in FIG. 2, the method is executed by a computing device.
- the computing device may be a computer device, or a terminal, such as a laser radar.
- the method includes the following steps:
- Step 110 Obtain echo waveform data.
- the laser radar works periodically, and at least one detection is completed in each detection cycle, that is, one corresponding transmission and reception, to obtain the echo waveform data x(n).
- the embodiment of the present invention does not specifically distinguish the specific application environment for obtaining the echo waveform data, and only needs to obtain the real-time echo waveform data according to the normal operation of the laser radar.
- Step 120 Comparing the similarity between the echo waveform data and the standard waveform data to obtain a comparison result.
- the standard waveform data is preset, and the echo waveform data is compared with the standard waveform data to obtain a comparison result, so as to determine the abnormal echo in the echo waveform data according to the comparison result.
- the calculation result is obtained by performing correlation calculation on the echo waveform data and the standard waveform data.
- the embodiment of the present invention presets standard waveform data.
- the echo data received by the laser radar at each point frequency is y(n), and its echo peak value is max(y(n)).
- max(y(n)) For the system AD bit is Bits, the range of max(y(n)) is Between 0 and 2 Bits -1, when the optical design and hardware of the lidar are fixed, the characteristics of the normal echo waveform will not change, only the peak position and peak size max(y(n)) of the echo will change, The peak position of the normal echo waveform will not affect the shape of the echo. When the peak size changes too much, it will slightly affect the shape of the echo.
- the echo data y(n) of the normal echo at all different peak amplitudes are collected, and a total of 2 Bits group echoes are taken.
- each group of normal echo waveforms includes the peak point of the normal echo waveform and the normal K points before and after the peak point of the echo waveform.
- the reasonable value of K depends on the design results of system hardware such as sampling rate and bandwidth.
- the standard waveform data includes standard amplitude deviations and standard energy values of the multiple groups of normal echo waveforms with different peak amplitudes.
- M_W_G y (m) represents the mean value of the amplitude of the mth group of normal echo waveforms W_G y (m).
- the amplitude mean value of the normal echo waveform is subtracted from the amplitude of the normal echo waveform to obtain the standard amplitude deviation corresponding to the normal echo waveform:
- W_G_R y (m, i) W_G y (m, i)-M_W_G y (m); in this formula, W_G_R y (m, i) represents the i-th waveform number in the m-th group of normal echo waveforms (also That is, the standard amplitude deviation corresponding to the i-th point).
- the standard waveform data is stored in the form of an array
- the standard amplitude deviation and standard energy value of each group of normal echo waveforms can be stored in the form of an array, which is stored as W_Ly ( m, i), where: the first 0 to M-1 values of the array W_L y (m, i) are the standard amplitude distances corresponding to the 0th to M-1 waveform numbers of the mth group of normal echo waveforms
- the Mth value is the standard energy value Mag y (m) of the mth group of normal echo waveforms.
- the amplitude W_G y (m) of the normal echo waveform is also stored in association with the array W_L y (m,i).
- associative storage refers to the data in relational relationship is connected and stored in a method to form a data structure with relational relation.
- related data can be represented by a data structure, such as an associative array;
- different data tables are linked through foreign keys and primary keys.
- the embodiment of the present invention first performs a Shift buffering, for the waveform sampling point n in the echo waveform data x(n), using the waveform sampling point n and K points before and after the waveform sampling point n as a group of echo waveform x( n-K: n+K). That is, each sampling point and K points before and after form a group of echo waveforms to be compared.
- correlation calculation is performed between the echo waveform x(nK:n+K) to be compared and the normal echo waveforms of each group to obtain a calculation result.
- calculate the mean value of the amplitude according to the current amplitude of the echo waveform to be compared: Subtract the mean value of the amplitude from the current amplitude of the echo waveform to be compared to obtain the current amplitude dispersion corresponding to the echo waveform to be compared: M_X_R(nK:n+K) x(nK:n+K)- M_X; Calculate the current energy value MagX(n) of the echo waveform to be compared according to the current amplitude deviation, where,
- the current amplitude deviation and current energy value of the echo waveform to be compared it is also determined in advance according to the current amplitude of the echo waveform x(nK,n+K) to be compared A target normal echo waveform corresponding to the current amplitude among multiple groups of normal echo waveforms with different peak amplitudes. Because the standard waveform data includes the amplitude of the normal echo waveform and the array W_L y (m,i).
- the correlation calculation is performed according to the current amplitude deviation, the current energy value, the standard amplitude deviation corresponding to the target normal echo waveform, and the standard energy value corresponding to the target normal echo waveform, specifically including :
- the correlation coefficient XcorrCoe(n) is calculated.
- the correlation coefficient is the calculation result, and the calculation formula is:
- Step 130 Determine the abnormal waveform in the echo waveform data according to the comparison result.
- the correlation coefficient is compared with the preset standard threshold, and when the correlation coefficient is smaller than the preset standard threshold, it indicates that the echo to be compared is not similar to the standard waveform data, and the echo to be compared is considered abnormal waveform.
- the correlation coefficient is not less than the preset standard threshold, it indicates that the echo to be compared is similar to the standard waveform data, and the echo to be compared is determined to be a normal waveform.
- the embodiment of the present invention does not specifically limit the specific value of the preset standard threshold, and those skilled in the art can make corresponding settings according to specific scenarios.
- Lidar can filter out abnormal waveforms, and perform data analysis in processes such as laser ranging based on normal waveforms.
- the waveform detection method in the embodiment of the present invention has high reliability and high robustness, and can adapt to detecting abnormal echoes under extreme weather or extreme conditions .
- the laser radar after obtaining the abnormal waveforms and normal waveforms in the echo waveform data, when there are many abnormal waveforms in the echo waveform data, the laser radar can issue an alarm prompt to remind the user that the current ranging waveform is abnormal .
- the embodiment of the present invention obtains the echo waveform data, and compares the echo waveform data with the preset standard waveform data to obtain the comparison result, and finally determines the abnormal waveform in the echo waveform data according to the comparison result, which can accurately It can accurately identify the normal echoes and abnormal echoes received by the lidar.
- multiple sets of echo waveforms to be compared are obtained, and correlation calculation is performed with multiple sets of normal echo waveforms with different peak amplitudes, which further improves the accuracy and efficiency of abnormal waveform identification.
- Fig. 3 shows a flow chart of a ranging method provided by an embodiment of the present invention, and the method is executed by a ranging device.
- the distance measuring device may be a laser radar device, or other devices that use optics for distance measurement.
- the method includes the following steps:
- Step 210 Send laser light to the target space with a preset frequency.
- Step 220 Receive echo waveform data corresponding to the laser.
- Step 230 Using the waveform detection method to determine abnormal waveforms and normal waveforms in the echo waveform data.
- the specific method steps of using the waveform detection method to determine the abnormal waveform and the normal waveform in the echo waveform data are generally consistent with the specific method steps of the above waveform detection method, and will not be repeated here.
- Step 240 Determine the target distance of the obstacle according to the normal waveform.
- the embodiment of the present invention obtains the echo waveform data, and compares the echo waveform data with the preset standard waveform data to obtain the comparison result, and finally determines the abnormal waveform in the echo waveform data according to the comparison result, which can accurately It can accurately identify the normal echoes and abnormal echoes received by the lidar. Furthermore, by preprocessing the echo waveform data, multiple sets of echo waveforms to be compared are obtained, and correlation calculation is performed with multiple sets of normal echo waveforms with different peak amplitudes, which further improves the accuracy and efficiency of abnormal waveform identification. The accuracy and reliability of ranging are improved.
- Fig. 4 shows a schematic structural diagram of a waveform detection device provided by an embodiment of the present invention.
- the device 300 includes:
- An acquisition module 310 configured to acquire echo waveform data.
- the calculation module 320 is used to compare the similarity between the echo waveform data and the standard waveform data to obtain a comparison result.
- a discrimination module 330 configured to determine abnormal waveforms in the echo waveform data according to the comparison result.
- the specific working process of the waveform detection device 300 in the embodiment of the present invention is the same as the specific method steps of the above-mentioned waveform detection method, and will not be repeated here.
- the embodiment of the present invention obtains the echo waveform data, and compares the echo waveform data with the preset standard waveform data to obtain the comparison result, and finally determines the abnormal waveform in the echo waveform data according to the comparison result, which can accurately It can accurately identify the normal echoes and abnormal echoes received by the lidar. Furthermore, by preprocessing the echo waveform data, multiple sets of echo waveforms to be compared are obtained, and correlation calculation is performed with multiple sets of normal echo waveforms with different peak amplitudes, which further improves the accuracy and efficiency of abnormal waveform identification. The accuracy and reliability of ranging are improved.
- An embodiment of the present invention also provides a laser radar, which includes the above-mentioned waveform detection device 300 .
- the lidar may be any one of solid-state lidar, mechanical lidar, hybrid solid-state lidar, solid-state lidar, and the like.
- the specific working process of the waveform detection device 300 in the lidar includes the specific method steps of the above-mentioned laser emission control method, which will not be repeated here.
- the embodiment of the present invention provides an automatic driving device including the laser radar in the above-mentioned embodiment.
- the automatic driving equipment includes a driving equipment body and the laser radar in the above embodiment, and the laser radar is installed on the automatic driving equipment body.
- An embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored on the computer-readable storage medium, and when the computer instructions are executed by a processor, the above waveform detection method is realized.
- An embodiment of the present invention provides a waveform detection device for performing the above waveform detection method.
- An embodiment of the present invention provides a computer program, and the computer program can be invoked by a processor to cause a waveform detection device to execute the waveform detection method in any method embodiment above.
- An embodiment of the present invention provides a computer program product.
- the computer program product includes a computer program stored on a computer-readable storage medium.
- the computer program includes program instructions.
- modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
- the modules or units or components in the embodiments can be combined into one module or unit or component, and they can be divided into a plurality of sub-modules or sub-units or sub-components. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined.
- Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
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Abstract
一种波形检测方法，波形检测方法包括: 获取回波波形数据; 将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果; 根据所述比较结果确定所述回波波形数据中的异常波形。通过上述方式，本发明实施例实现了准确识别异常回波的效果。
Description
本发明实施例涉及激光雷达技术领域，具体涉及一种波形检测方法、测距方法、波形检测装置、激光雷达、自动驾驶设备及计算机可读存储介质。
激光雷达是通过发射激光进行测量的设备。由于激光雷达工作环境中干扰的存在，如在恶劣的自然条件下或近距离因光在内腔多次反射的干扰引起的距离吸入等，使得激光雷达接收到的回波中存在大量的干扰数据，这些情况产生的回波，其幅值有大有小，会使得激光雷达感知或决策出现误判断。
发明内容
鉴于上述问题，本发明实施例提供了一种波形检测方法，用于解决现有技术中存在的无法准确识别异常波形的技术问题。
根据本发明实施例的一个方面，提供了一种波形检测方法，所述方法包括：
获取回波波形数据；
将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果；
根据所述比较结果确定所述回波波形数据中的异常波形。
在一种可选的方式中，所述将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果，包括：将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果；根据所述计算结果确定所述比较结果。
在一种可选的方式中，所述将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果，包括：根据所述标准波形数据的标准幅值离差、所述标准波形数据的标准能量值、所述回波波形数据的幅值离差及所述回波波形数据的能量值，进行相关性计算，得到计算结果。
在一种可选的方式中，所述标准波形数据包括多组不同峰值幅度的正常回波波形，每组所述正常回波波形包括所述正常回波波形的峰值点及所述正常回波波形的峰值点前后K个点；所述将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果，包括：确定所述回波波形数据中的各个波形采样点；将所述波形采样点以及所述波形采样点的前后K个点作为一组待比较回波波形；将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结 果。
在一种可选的方式中，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果之前，所述方法包括：计算所述正常回波波形的幅值均值；将所述正常回波波形的幅值减去所述正常回波波形的幅值均值，得到所述正常回波波形对应的标准幅值离差；根据所述标准幅值离差计算所述正常回波波形的标准能量值。
在一种可选的方式中，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果，包括：计算所述待比较回波波形的幅值均值；将所述待比较回波波形的当前幅值减去所述幅值均值，得到所述待比较回波波形对应的当前幅值离差；根据所述当前幅值离差计算所述待比较回波波形的当前能量值。
在一种可选的方式中，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果，包括：根据所述当前幅值，确定所述多组不同峰值幅度的正常回波波形中与所述当前幅值对应的目标正常回波波形；根据所述当前幅值离差、所述当前能量值、所述目标正常回波波形对应的标准幅值离差以及所述目标正常回波波形对应的标准能量值进行相关性计算，得到计算结果。
在一种可选的方式中，所述根据所述当前幅值离差、所述当前能量值、所述目标正常回波波形对应的标准幅值离差以及所述目标正常回波波形对应的标准能量值进行相关性计算，得到计算结果，包括：根据所述当前能量值与所述目标正常回波波形对应的标准能量值，得到能量相关结果；根据所述当前幅值离差及所述目标正常回波波形对应的标准幅值离差，得到幅值相关结果；根据所述能量相关结果及所述幅值相关结果，计算得到相关系数。
根据本发明实施例的另一方面，提供了一种测距方法，包括：
以预设频率向目标空间发射激光；
接收所述激光对应的回波波形数据；
采用所述的波形检测方法确定所述回波波形数据中的异常波形及正常波形；
根据所述正常波形确定障碍物的目标距离。
根据本发明实施例的另一方面，提供了一种波形检测装置，包括：
获取模块，用于获取回波波形数据；
计算模块，用于将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果；
判别模块，用于根据所述比较结果确定所述回波波形数据中的异常波形。
根据本发明实施例的另一方面，提供了一种激光雷达，所述激光雷达包括所述的波形检测装置。
根据本发明实施例的另一方面，提供了一种自动驾驶设备，包括驾驶设备本体以及所述的激光雷达，所述激光雷达安装于所述驾驶设备本体。
根据本发明实施例的又一方面，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上存储计算机指令，所述计算机指令被处理器执行时实现所述的波形检测方法。
本发明实施例通过获取回波波形数据，将回波波形数据与预设的标准波形数据进行相似比较，从而得到比较结果，最后根据所述比较结果确定回波波形数据中的异常波形，能够准确地识别出激光雷达接收到的正常回波及异常回波。
进一步地，通过将回波波形数据进行预处理，得到多组待比较回波波形，与多组不同峰值幅度的正常回波波形进行相关性计算，进一步提高了异常波形识别的准确率及效率。
上述说明仅是本发明实施例技术方案的概述，为了能够更清楚了解本发明实施例的技术手段，而可依照说明书的内容予以实施，并且为了让本发明实施例的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图仅用于示出实施方式，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了本发明实施例提供的波形检测方法的应用环境示意图；
图2示出了本发明实施例提供的波形检测方法的流程示意图；
图3示出了本发明另一实施例提供的测距方法的流程示意图；
图4示出了本发明实施例提供的波形检测装置的结构示意图。
下面将参照附图更详细地描述本发明的示例性实施例。虽然附图中显示了本发明的示例性实施例，然而应当理解，可以以各种形式实现本发明而不应被这里阐述的实施例所限制。
本发明实施例提供的波形检测方法应用于激光雷达，具体可以由激光雷达的控制及处理单元执行。如图1所示，激光雷达的光电传感器在空中发射不同垂直角度的激光束，同时光电传感器接收到目标返回来的回波，转化为微弱的电信号，该微弱的电信号放大后，经模数变换，在数字域计算目标的距离。激光雷达的控制及处理单元用于控制系统按照一定的发射接收时序来工作，同时对接收的回波数据处理得到目标的距离结果，发射单元通常是多个半导体激光 器阵列，在电压的驱动下按照一定的时序发射激光，当发射方向有目标时，返回的回波经接收镜头到接收光电传感器，转化为电信号，在接收单元放大转化为数字量，经过后续的数字化处理后形成距离结果。
现有激光雷达的应用场景越来越多，这使得激光雷达不得不在各种复杂的情况下工作，而在异常情况下产生的异常回波对雷达激光在探测分析时的判断产生影响，使得激光雷达无法正确识别。例如，恶劣的自然条件、物体过渡处产生的拖点、或近距离因光在内腔多次反射的干扰引起的距离吸入等情况下产生的异常回波。具体地，在恶劣的自然条件下工作时，对于雨、雾、灰霾等产生的干扰回波，其幅值有大有小，不低于正常回波，当该干扰回波能够过门限时，会被激光雷达当做正常回波，从而形成虚假的点云，这些虚假点云会使得感知或决策出现误判断。对于纵向上较近的两个物体，当光束扫描顺序经过时，由于光束横向有一定的角度分布，经过两个物体时存在过渡暂态，逐渐从一个距离过渡到另外一个距离，形成的过渡点云称为物体之间的拖点，这些拖点引起的干扰同样会使得激光雷达在测距过程中出现判断错误。另外，对于近距离的低反目标，光在机器内腔多次反射，这些多余的光学成分都是无用的干扰成分，他们和近距离真实的目标回波靠的非常近，甚至连在一起，也影响到了真实的正常回波的检测。
基于上述异常回波导致的检测结果错误的问题，本发明实施例提供了一种波形检测方法。如图2所示，该方法由计算设备执行。该计算设备可以是计算机设备，还可以是终端，如激光雷达等。该方法包括以下步骤：
步骤110：获取回波波形数据。
其中，激光雷达周期性地进行工作，在每个探测周期内完成至少一次探测，即一次对应的发射和接收，得到回波波形数据x(n)。本发明实施例并不具体区分获取回波波形数据的具体应用环境，只需要按照激光雷达正常工作来获取实时的回波波形数据即可。
步骤120：将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果。
考虑到正常情况下的正常回波，例如地面，墙，车辆，行人，树木等，它们的回波形状相似度非常高；而对于在存在干扰的异常情况下的异常回波，前沿或者后延形状都有明显的变异，这些异常回波与正常回波的相似度较低。因此，可以根据正常回波和异常回波之间的相似度，来不加区分的识别出这些异常回波。因此，本发明实施例中预先设置标准波形数据，将回波波形数据与标准波形数据进行相似度比较，得到比较结果，以根据比较结果来确定该回波波形数据中的异常回波。
本发明实施例中，通过将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果。
其中，本发明实施例预设了标准波形数据。激光雷达每次点频接收到的回 波数据为y(n)，其回波峰值为max(y(n))，对于系统AD位数为Bits，则max(y(n))的范围为0到2
Bits-1之间，当激光雷达的光学设计和硬件固定时，正常回波波形的特性就不会变化，仅回波的峰值位置以及峰值大小max(y(n))有变化，正常回波波形的峰值位置不会影响到回波的形状。峰值大小变化过大时，对回波的形状稍有影响。因此，按照增益逐渐增加的方式，采集正常回波在所有不同峰值幅度下的回波数据y(n)，一共取2
Bits组回波，对于每组回波，取峰值位置左边，右边各K个点，即一共M＝2K+1个点，从而得到2
Bits组不同峰值幅度的正常回波波形W_G
y(m)，每组正常回波波形包括该正常回波波形的峰值点及该正常回波波形的峰值点前后的K个点。其中，K的合理值视系统硬件的设计结果如采样率和带宽而定。对该2
Bits组不同峰值幅度的正常回波波形进行数据预处理，从而得到标准波形数据。该标准波形数据包括该多组不同峰值幅度的正常回波波形的标准幅值离差及标准能量值。
具体地，首先计算每组正常回波波形的幅值均值：
然后，将该正常回波波形的幅值减去该正常回波波形的幅值均值，得到该正常回波波形对应的标准幅值离差：
W_G_R
y(m，i)＝W_G
y(m，i)-M_W_G
y(m)；该公式中，W_G_R
y(m，i)表征第m组正常回波波形中的第i个波形序号(也即第i个点)所对应的标准幅值离差。
最后，根据该标准幅值离差计算该第m组正常回波波形的标准能量值为：
本发明实施例中，该标准波形数据以数组的形式存储，具体地，将每组正常回波波形的标准幅值离差及标准能量值可以以数组的形式存储，将其存储为W_L
y(m,i)，其中：该数组W_L
y(m,i)的前0到M-1个数值为第m组正常回波波形的第0到M-1个波形序号所对应的标准幅值离差，第M个数值为该第m组正常回波波形的标准能量值Mag
y(m)。其中，在存储时，还将正常回波波形的幅值W_G
y(m)与数组W_L
y(m,i)关联存储。其中,关联存储指的是关联关系的数据通过一种方法联系起来再存储，形成具有关联关系的数据结构，例如，可将有关联的数据用一种数据结构表示，如关联数组；还可以在数据库中通过外键和主键将不同数据表联系起来。通过预先将标准波形数据以数组的形式存储至服务器，从而使得提高了数据运算效率，从而使得波形检测更加高效。
对于实时探测得到的回波波形数据x(n)，其可能为正常，也可能异常的波形。由于无法预先了解该回波波形数据x(n)的峰值，且为了进行波形与波形之间的相似度比较，因此本发明实施例对回波波形数据x(n)中的每个点先进行移位缓存，对于所述回波波形数据x(n)中的波形采样点n，将所述波形采样点n 以及该波形采样点n的前后K个点作为一组待比较回波波形x(n-K：n+K)。也即，每一个采样点与前后K个点形成一组待比较回波波形。
在得到待比较回波波形后，将所述待比较回波波形x(n-K：n+K)与各组所述正常回波波形进行相关性计算，得到计算结果。进行相关性计算时，根据待比较回波波形的当前幅值计算其幅值均值：
将待比较回波波形的当前幅值减去所述幅值均值，得到待比较回波波形对应的当前幅值离差：M_X_R(n-K：n+K)＝x(n-K：n+K)-M_X；根据当前幅值离差计算待比较回波波形的当前能量值MagX(n)，其中，
为了提高计算效率，本发明实施例在得到待比较回波波形的当前幅值离差及当前能量值后，还预先根据待比较回波波形x(n-K,n+K)的当前幅值来确定多组不同峰值幅度的正常回波波形中与当前幅值对应的目标正常回波波形。由于标准波形数据中包括正常回波波形的幅值及数组W_L
y(m,i)。通过预先根据当前幅值在数组W_L
y(m,i)表中查询确定目标正常回波波形的标准幅值离差xLut(0：M-1)及标准能量值xLut(M)，使得不必要将待比较回波波形分别与每一个正常回波波形进行相关性计算，从而提高了数据处理的效率。
在得到目标正常回波波形后，根据当前幅值离差、当前能量值、目标正常回波波形对应的标准幅值离差以及目标正常回波波形对应的标准能量值进行相关性计算，具体包括：
首先，根据当前能量值与目标正常回波波形对应的标准能量值，得到能量相关结果。具体地，计算当前能量值与目标正常回波波形对应的标准能量值的乘积，得到乘积结果Emul(n)：Emul(n)＝MagX(n)*xLut(M)。对该乘积结果进行开方，得到能量相关结果
然后，根据当前幅值离差及目标正常回波波形对应的标准幅值离差，得到幅值相关结果Xcorr(n)：
最后，根据能量相关结果及幅值相关结果，计算得到相关系数XcorrCoe(n)，该相关系数即为计算结果，计算公式为：
步骤130：根据比较结果确定回波波形数据中的异常波形。
其中，在得到计算结果后，根据相关系数与预设标准阈值进行比较，当相关系数小于预设标准阈值时，表征该待比较回波与标准波形数据不相似，则认为待比较回波为异常波形。当相关系数不小于预设标准阈值时，表征该待比较回波与标准波形数据相似，则确定该待比较回波为正常波形。通过对回波波形数据中各个点进行移位缓存再计算相关性，从而确定了回波波形数据中的异常波形及正常波形。本发明实施例并不具体限定预设标准阈值的具体数值，本领 域技术人员可依据具体场景进行相应设置。激光雷达可过滤掉异常波形，根据正常波形来进行激光测距等过程中的数据分析。通过待比较回波与预存的标准波形数据进行波形的相关性比较，使得本发明实施例的波形检测方法具有高可靠性及高鲁棒性，能够适应检测极端天气或极端条件下的异常回波。
本发明实施例中，在得到了回波波形数据中的异常波形及正常波形后，当回波波形数据中的异常波形较多时，激光雷达可发出报警提示，以提醒用户当前测距的波形异常。
本发明实施例通过获取回波波形数据，将回波波形数据与预设的标准波形数据进行相似比较，从而得到比较结果，最后根据所述比较结果确定回波波形数据中的异常波形，能够准确地识别出激光雷达接收到的正常回波及异常回波。
进一步地，通过将回波波形数据进行预处理，得到多组待比较回波波形，与多组不同峰值幅度的正常回波波形进行相关性计算，进一步提高了异常波形识别的准确率及效率。
图3示出了本发明实施例提供的测距方法的流程图，该方法由测距设备执行。该测距设备可以是激光雷达设备，还可以是其它利用光学进行测距的设备。如图3所示，该方法包括以下步骤：
步骤210：以预设频率向目标空间发射激光。
步骤220：接收所述激光对应的回波波形数据。
步骤230：采用所述的波形检测方法确定所述回波波形数据中的异常波形及正常波形。本发明实施例中，采用所述的波形检测方法确定所述回波波形数据中的异常波形及正常波形的具体方法步骤，与上述波形检测方法的具体方法步骤大体一致，此处不再赘述。
步骤240：根据所述正常波形确定障碍物的目标距离。
本发明实施例通过获取回波波形数据，将回波波形数据与预设的标准波形数据进行相似比较，从而得到比较结果，最后根据所述比较结果确定回波波形数据中的异常波形，能够准确地识别出激光雷达接收到的正常回波及异常回波。进一步地，通过将回波波形数据进行预处理，得到多组待比较回波波形，与多组不同峰值幅度的正常回波波形进行相关性计算，进一步提高了异常波形识别的准确率及效率，提高了测距的准确率及可靠性。
图4示出了本发明实施例提供的波形检测装置的结构示意图。如图4所示，该装置300包括：
获取模块310，用于获取回波波形数据。
计算模块320，用于将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果。
判别模块330，用于根据所述比较结果确定所述回波波形数据中的异常波形。
本发明实施例的波形检测装置300的具体工作过程与上述波形检测方法的具体方法步骤相同，此处不再赘述。
本发明实施例通过获取回波波形数据，将回波波形数据与预设的标准波形数据进行相似比较，从而得到比较结果，最后根据所述比较结果确定回波波形数据中的异常波形，能够准确地识别出激光雷达接收到的正常回波及异常回波。进一步地，通过将回波波形数据进行预处理，得到多组待比较回波波形，与多组不同峰值幅度的正常回波波形进行相关性计算，进一步提高了异常波形识别的准确率及效率，提高了测距的准确率及可靠性。
本发明的实施例还提供一种激光雷达，该激光雷达包括上述的波形检测装置300。其中，该激光雷达可以是固体激光雷达、机械式激光雷达、混合固态激光雷达、固态激光雷达等中的任意一种。该激光雷达中波形检测装置300的具体工作过程包括上述激光发射控制方法的具体方法步骤，此处不再赘述。
更进一步的，基于上述激光雷达，本发明实施例提供一种包含上述实施例中的激光雷达的自动驾驶设备，该自动驾驶设备可以是汽车、飞机、船以及其他涉及到使用激光雷达进行智能感应和探测的设备，该自动驾驶设备包括驾驶设备本体以及如上实施例的激光雷达，激光雷达安装于该自动驾驶设备本体。
本发明实施例提供了一种计算机可读存储介质，该计算机可读存储介质上存储计算机指令，该计算机指令被处理器执行时实现上述的波形检测方法。
本发明实施例提供一种波形检测装置，用于执行上述波形检测方法。
本发明实施例提供了一种计算机程序，所述计算机程序可被处理器调用使波形检测设备执行上述任意方法实施例中的波形检测方法。
本发明实施例提供了一种计算机程序产品，计算机程序产品包括存储在计算机可读存储介质上的计算机程序，计算机程序包括程序指令，当程序指令在计算机上运行时，使得所述计算机执行上述任意方法实施例中的波形检测方法。
在此提供的算法或显示不与任何特定计算机、虚拟系统或者其它设备固有相关。各种通用系统也可以与基于在此的示教一起使用。根据上面的描述，构造这类系统所要求的结构是显而易见的。此外，本发明实施例也不针对任何特定编程语言。应当明白，可以利用各种编程语言实现在此描述的本发明的内容，并且上面对特定语言所做的描述是为了披露本发明的最佳实施方式。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本发明并帮助理解各个发明方面中的一个或多个，在上面对本发明的示例性实施例的描述中，本发明实施例的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该公开的方法解释成反映如下意图：即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。
本领域技术人员可以理解，可以对实施例中的设备中的模块进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的模块或单元或组件组合成一个模块或单元或组件，以及可以把它们分成多个子模块或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。上述实施例中的步骤，除有特殊说明外，不应理解为对执行顺序的限定。
Claims (13)
- 一种波形检测方法，其特征在于，所述方法包括：获取回波波形数据；将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果；根据所述比较结果确定所述回波波形数据中的异常波形。
- 根据权利要求1所述的方法，其特征在于，所述将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果，包括：将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果；根据所述计算结果确定所述比较结果。
- 根据权利要求2所述的方法，其特征在于，所述将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果，包括：根据所述标准波形数据的标准幅值离差、所述标准波形数据的标准能量值、所述回波波形数据的幅值离差及所述回波波形数据的能量值，进行相关性计算，得到计算结果。
- 根据权利要求2所述的方法，其特征在于，所述标准波形数据包括多组不同峰值幅度的正常回波波形，每组所述正常回波波形包括所述正常回波波形的峰值点及所述正常回波波形的峰值点前后K个点；所述将所述回波波形数据与标准波形数据进行相关性计算，得到计算结果，包括：确定所述回波波形数据中的各个波形采样点；将所述波形采样点以及所述波形采样点的前后K个点作为一组待比较回波波形；将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果。
- 根据权利要求4所述的方法，其特征在于，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果之前，所述方法包括：计算所述正常回波波形的幅值均值；将所述正常回波波形的幅值减去所述正常回波波形的幅值均值，得到所述 正常回波波形对应的标准幅值离差；根据所述标准幅值离差计算所述正常回波波形的标准能量值。
- 根据权利要求5所述的方法，其特征在于，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果，包括：计算所述待比较回波波形的幅值均值；将所述待比较回波波形的当前幅值减去所述幅值均值，得到所述待比较回波波形对应的当前幅值离差；根据所述当前幅值离差计算所述待比较回波波形的当前能量值。
- 根据权利要求6所述的方法，其特征在于，所述将所述待比较回波波形与各组所述正常回波波形进行相关性计算，得到计算结果，包括：根据所述当前幅值，确定所述多组不同峰值幅度的正常回波波形中与所述当前幅值对应的目标正常回波波形；根据所述当前幅值离差、所述当前能量值、所述目标正常回波波形对应的标准幅值离差以及所述目标正常回波波形对应的标准能量值进行相关性计算，得到计算结果。
- 根据权利要求7所述的方法，其特征在于，所述根据所述当前幅值离差、所述当前能量值、所述目标正常回波波形对应的标准幅值离差以及所述目标正常回波波形对应的标准能量值进行相关性计算，得到计算结果，包括：根据所述当前能量值与所述目标正常回波波形对应的标准能量值，得到能量相关结果；根据所述当前幅值离差及所述目标正常回波波形对应的标准幅值离差，得到幅值相关结果；根据所述能量相关结果及所述幅值相关结果，计算得到相关系数。
- 一种测距方法，其特征在于，所述方法包括：以预设频率向目标空间发射激光；接收所述激光对应的回波波形数据；采用如权利要求1至8任一项所述的波形检测方法确定所述回波波形数据 中的异常波形及正常波形；根据所述正常波形确定障碍物的目标距离。
- 一种波形检测装置，其特征在于，所述装置包括：获取模块，用于获取回波波形数据；计算模块，用于将所述回波波形数据与标准波形数据进行相似度比较，得到比较结果；判别模块，用于根据所述比较结果确定所述回波波形数据中的异常波形。
- 一种激光雷达，其特征在于，所述激光雷达包括如权利要求10所述的波形检测装置。
- 一种自动驾驶设备，其特征在于，包括驾驶设备本体以及如权利要求11所述的激光雷达，所述激光雷达安装于所述驾驶设备本体。
- 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储计算机指令，所述计算机指令被处理器执行时实现如权利要求1至8任一项所述的波形检测方法。
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