Laser radar laser energy adjusting method, device and equipment and storage medium

Abstract

The invention discloses a laser radar laser energy adjusting method, device and equipment and a storage medium, and relates to the technical field of laser radars. The laser radar laser energy adjustment method comprises the following steps: acquiring laser emission energy of a historical distance measurement point, a distance calculation result of the historical distance measurement point and an energy value of a reflected pulse of the historical distance measurement point to obtain a sample matrix; inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; and inputting the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next distance measurement point. According to the invention, the distance calculation precision can be improved by adjusting the laser.

Description

Technical Field

[0001] The present application relates to the field of laser radar technology, and in particular to a laser radar laser energy regulation method, device, equipment and storage medium. Background Art

[0002] LiDAR (Light Detection and Ranging) is an active measurement device that measures the precise distance between an object and a sensor by emitting a laser beam. In practical applications, the intensity of the echo signal received by the receiver may vary greatly due to the large differences in the reflectivity of different obstacles and their relative distances from the LiDAR. In order to ensure that the received signal is within the optimal operating range of the receiver and to ensure the accuracy of the distance solution, it is necessary to adjust the laser emission energy.

[0003] At present, the laser emission energy adjustment scheme is based on the weighted calculation of the previous or previous laser reflection energy data. However, this scheme can hardly ensure that the next laser reflection energy is within the appropriate range, and cannot cope with the situation of sudden changes in the reflectivity or distance of the detection object, and the distance solution accuracy is not high. Therefore, how to improve the distance solution accuracy by adjusting the laser is a problem that still needs to be solved.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a lidar laser energy adjustment method, device, equipment and storage medium, aiming to solve the technical problem of how to improve the distance solution accuracy by adjusting the laser.

[0006] To achieve the above objectives, the present application proposes a method for adjusting laser energy of a laser radar, the method comprising: Obtain the laser emission energy of the historical ranging points, the distance solution results of the historical ranging points, and the energy values ​​of the reflected pulses of the historical ranging points to obtain a sample matrix; Inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0007] In one embodiment, the step of inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model includes: According to the sample matrix, calculate the target value and output value; Calculating an offset between the target value and the output value; According to the offset, a laser output energy prediction model is obtained.

[0008] In one embodiment, the step of calculating the target value and the output value according to the sample matrix includes: According to the sample matrix and the preset algorithm, the target value is obtained; According to the sample matrix, the output of the hidden layer and output layer of the neural network is calculated to obtain the output value.

[0009] In one embodiment, the step of obtaining the target value according to the sample matrix and a preset algorithm includes: Get the lower limit and upper limit of the energy value; A target value is obtained according to the energy value of the reflected pulse at the historical ranging point, the lower limit of the energy value, and the upper limit of the energy value.

[0010] In one embodiment, the step of obtaining the target value according to the energy value of the reflected pulse at the historical ranging point, the lower limit of the energy value, and the upper limit of the energy value includes: When the energy value of the reflected pulse at the historical ranging point is less than the lower limit of the energy value, the The lower limit of the energy value is taken as the target value; When the energy value of the reflected pulse at the historical ranging point is greater than the upper limit of the energy value, the The upper limit of the energy value is used as the target value; When the energy value of the reflected pulse at the historical ranging point is between the lower limit of the energy value and the upper limit of the energy value, the energy value of the reflected pulse at the historical ranging point is used as the target value.

[0011] In one embodiment, the step of obtaining a laser output energy prediction model according to the offset comprises: If the offset is within the preset allowable range, the training is terminated to obtain a laser output energy prediction model; If the offset is not within the preset allowable range, the weights of the hidden layer and the output layer of the neural network are updated, and the output value is recalculated, and the process returns to the step of calculating the offset between the target value and the output value.

[0012] In one embodiment, the step of obtaining a laser output energy prediction model according to the offset comprises: If the offset is within the preset allowable range, the training is terminated to obtain a laser output energy prediction model; If the offset is not within the preset allowable range, the weights of the hidden layer and the output layer of the neural network are updated, and the output value is recalculated, and the process returns to the step of calculating the offset between the target value and the output value.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a laser radar laser energy adjustment device, the laser radar laser energy adjustment device comprising: An acquisition module acquires the laser emission energy of the ranging point, the distance solution result of the ranging point, and the energy value of the reflected pulse of the ranging point to obtain a sample matrix; A training module, inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The calculation module inputs the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0014] In addition, to achieve the above-mentioned objectives, the present application also proposes a laser radar laser energy adjustment device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the laser radar laser energy adjustment method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the lidar laser energy adjustment method as described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the lidar laser energy adjustment method as described above.

[0017] The present application provides a method for regulating the energy of a lidar laser. The present application first obtains the laser emission energy of a historical ranging point, the distance solution result of the historical ranging point, and the energy value of the reflected pulse of the historical ranging point to obtain a sample matrix; the sample matrix is ​​input into a neural network for training to obtain a laser output energy prediction model; the sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0018] In summary, the present application performs real-time training on the neural network, and uses the neural network to predict the laser output energy required for the next ranging point, which can ensure with a high probability that the laser echo pulse signal is within the ideal range and improve the distance solution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A schematic diagram of a flow chart provided for Embodiment 1 of the laser radar laser energy regulation method of the present application; Figure 2 A schematic diagram of a flow chart provided for Embodiment 2 of the laser radar laser energy regulation method of the present application; Figure 3 This is a schematic diagram of the module structure of the laser radar laser energy adjustment device of the embodiment of the present application; Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the lidar laser energy adjustment method in the embodiment of the present application.

[0022] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of the embodiment of the present application is: obtain the laser emission energy of the historical ranging point, the distance solution result of the historical ranging point and the energy value of the reflected pulse of the historical ranging point to obtain a sample matrix; input the sample matrix into the neural network for training to obtain a laser output energy prediction model; input the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0026] At present, the existing laser emission energy regulation method is based on the weighted calculation of the previous or previous laser reflection energy data. However, this solution can hardly ensure that the next laser reflection energy is within an appropriate range, and cannot cope with the situation of sudden changes in the reflectivity or distance of the detection object, and the distance solution accuracy is not high.

[0027] This application performs real-time training on the neural network, and uses the neural network to predict the laser output energy required for the next ranging point, which can ensure that the laser echo pulse signal is within the ideal range with a high probability, thereby improving the accuracy of distance calculation.

[0028] Based on this, the embodiment of the present application provides a laser radar laser energy adjustment method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the laser radar laser energy adjustment method of the present application.

[0029] In this embodiment, the laser radar laser energy adjustment method includes steps S10 to S30: Step S10: Obtain the laser emission energy of the historical ranging points, the distance solution results of the historical ranging points, and the energy values ​​of the reflected pulses of the historical ranging points to obtain a sample matrix; It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a laser radar host computer, etc. The following takes the laser radar host computer as an example to illustrate this embodiment and the following embodiments.

[0030] It should be noted that the historical ranging point refers to the point where the ranging has been completed. By obtaining the laser emission energy of the historical ranging point, the distance solution result of the historical ranging point, and the energy value of the reflected pulse of the historical ranging point, the sample matrix can be obtained. The sample matrix is ​​as follows:

[0031] Wherein, S(n) is the laser emission energy of the previous ranging point; L(n) is the distance solution result of the previous ranging point; E(n) is the energy value of the reflected pulse of the previous ranging point; and m is a positive integer.

[0032] Step S20: inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; It should be noted that a neural network is composed of a large number of nodes that are interconnected in the network and can process complex data inputs and perform various tasks such as classification, regression, pattern recognition, etc. The training of a neural network is to optimize the performance of the network by adjusting the parameters in the network (such as weights and biases) so that it can accurately predict or classify the input data. The sample matrix is ​​input into the neural network for training, and finally a laser output energy prediction model is obtained.

[0033] Step S30: inputting the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0034] It should be noted that the sample matrix is ​​input into the laser output energy prediction model, and the laser output energy prediction model processes the data in the sample matrix to obtain the laser output energy of the next ranging point. At the same time, the obtained output energy can be used as a sample matrix for real-time training, ensuring the accuracy of the laser output energy prediction model.

[0035] This embodiment first obtains the laser emission energy of the historical ranging point, the distance solution result of the historical ranging point, and the energy value of the reflected pulse of the historical ranging point to obtain a sample matrix; the sample matrix is ​​input into the neural network for training to obtain a laser output energy prediction model; the sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point. This embodiment trains the neural network in real time, and predicts the laser output energy required for the next ranging point through the neural network, which can ensure that the laser echo pulse signal is within the ideal range with a high probability, thereby improving the accuracy of distance solution.

[0036] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S20 may include steps S201 to S203: Step S201: Calculate the target value and output value according to the sample matrix; It should be noted that the target value refers to the more accurate value obtained directly through the sample matrix, while the output value is the value calculated and output by the neural network.

[0037] In one possible manner, the step of calculating the target value and the actual value according to the sample matrix includes: According to the sample matrix and the preset algorithm, the target value is obtained; According to the sample matrix, the output of the hidden layer and output layer of the neural network is calculated to obtain the actual value.

[0038] It should be noted that, in order to obtain the target value according to the preset algorithm, it is first necessary to obtain the lower limit of the energy value and the upper limit of the energy value. Then, the target value is obtained according to the energy value of the reflected pulse of the historical ranging point, the lower limit of the energy value and the upper limit of the energy value. The specific process is that when the energy value of the reflected pulse of the historical ranging point is less than the lower limit of the energy value, the lower limit of the energy value is used as the target value; when the energy value of the reflected pulse of the historical ranging point is greater than the upper limit of the energy value, the upper limit of the energy value is used as the target value; when the energy value of the reflected pulse of the historical ranging point is between the lower limit of the energy value and the upper limit of the energy value, the energy value of the reflected pulse of the historical ranging point is used as the target value. The specific formula is:

[0039]

[0040] Where T(n) is the target value, E(n) is the energy value of the reflected pulse at the previous distance measurement point, E0 is the lower limit of the energy value, and E1 is the upper limit of the energy value. k is the laser emission energy adjustment coefficient, which is a predetermined constant. Within this energy range, E(n) has the best distance calculation accuracy.

[0041] Step S202: Calculate the offset between the target value and the output value; Calculating the offset between the target value and the output value is to calculate the difference between the target value and the output value. Through the offset, it can be determined whether the current output of the neural network meets the requirements.

[0042] Step S203: obtaining a laser output energy prediction model according to the offset.

[0043] The offset is used to determine whether the current output of the neural network meets the requirements, and then a decision is made based on the judgment result whether to iterate, and finally a laser output energy prediction model can be obtained.

[0044] In a feasible manner, the step of obtaining a laser output energy prediction model according to the offset includes: If the offset is within the preset allowable range, the training is terminated to obtain a laser output energy prediction model; If the offset is not within the preset allowable range, the weights of the hidden layer and the output layer of the neural network are updated, and the actual value is recalculated, and the process returns to the step of calculating the offset between the target value and the actual value.

[0045] It is understandable that when the offset is within the preset allowable range, it means that the difference between the target value and the output value is very small and has met the requirements. At this time, the laser output energy prediction model can be obtained. When the offset is not within the preset allowable range, it means that the difference between the target value and the output value is large and does not meet the requirements. It is necessary to update the weights of the hidden layer and the output layer of the neural network, and recalculate the output value to obtain a new offset. Until the offset meets the error requirements. The steps of updating the weights of the hidden layer and the output layer of the neural network can be divided into calculating the error between the hidden layer and the output layer of the neural network; calculating the error gradient based on the error; and updating the weights of the hidden layer and the output layer of the neural network based on the error gradient.

[0046] In addition, in order to deal with the situation where the iteration cannot be stopped, it is necessary to obtain the maximum number of iterations, and each time an iteration is performed, it is necessary to determine whether the current number of iterations exceeds the maximum number of iterations. If the maximum number of iterations has been reached, the iteration needs to be stopped.

[0047] This embodiment first calculates the target value and the actual value based on the sample matrix; calculates the offset between the target value and the actual value; and finally obtains the laser output energy prediction model based on the offset. The whole process trains the neural network, adjusts the parameters in the neural network through continuous iteration, and finally obtains the laser output energy prediction model. The laser output energy prediction model thus obtained can accurately derive the next laser output energy and improve the distance solution accuracy.

[0048] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the laser radar laser energy adjustment method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0049] This application also provides a laser radar laser energy adjustment device, please refer to Figure 3 , the laser radar laser energy adjustment device comprises: An acquisition module 10 acquires the laser emission energy of the ranging point, the distance solution result of the ranging point, and the energy value of the reflected pulse of the ranging point to obtain a sample matrix; A training module 20, inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The calculation module 30 inputs the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0050] In one embodiment, the training module 20 is further used to calculate a target value and an actual value according to a sample matrix; calculate an offset between the target value and the actual value; and obtain a laser output energy prediction model according to the offset.

[0051] In one embodiment, the training module 20 is further used to obtain a target value according to a sample matrix and a preset algorithm; and to calculate the output of a hidden layer and an output layer of the neural network according to the sample matrix to obtain an actual value.

[0052] In one embodiment, the training module 20 is further used to obtain a lower limit and an upper limit of the energy value; and obtain a target value according to the energy value of the reflected pulse of the historical ranging point, the lower limit of the energy value and the upper limit of the energy value.

[0053] In one embodiment, the training module 20 is further used to, when the energy value of the reflected pulse at the historical ranging point is less than the lower limit of the energy value, use the lower limit of the energy value as the target value; when the energy value of the reflected pulse at the historical ranging point is greater than the upper limit of the energy value, use the upper limit of the energy value as the target value; when the energy value of the reflected pulse at the historical ranging point is between the lower limit of the energy value and the upper limit of the energy value, use the energy value of the reflected pulse at the historical ranging point as the target value.

[0054] In one embodiment, the training module 20 is also used to terminate the training and obtain a laser output energy prediction model if the offset is within a preset allowable range; if the offset is not within the preset allowable range, update the weights of the hidden layer and the output layer of the neural network, recalculate the actual value, and return to the step of calculating the offset between the target value and the actual value.

[0055] In one embodiment, the training module 20 is further used to calculate the error between the hidden layer and the output layer of the neural network; calculate the error gradient based on the error; and update the weights of the hidden layer and the output layer of the neural network based on the error gradient.

[0056] This embodiment first obtains the laser emission energy of the historical ranging point, the distance solution result of the historical ranging point, and the energy value of the reflected pulse of the historical ranging point to obtain a sample matrix; the sample matrix is ​​input into the neural network for training to obtain a laser output energy prediction model; the sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point. This embodiment trains the neural network in real time, and predicts the laser output energy required for the next ranging point through the neural network, which can ensure that the laser echo pulse signal is within the ideal range with a high probability, thereby improving the accuracy of distance solution.

[0057] The laser radar laser energy adjustment device provided by the present application adopts the laser radar laser energy adjustment method in the above embodiment, which can solve the technical problem of how to improve the distance solution accuracy by adjusting the laser. Compared with the prior art, the beneficial effects of the laser radar laser energy adjustment device provided by the present application are the same as the beneficial effects of the laser radar laser energy adjustment method provided by the above embodiment, and the other technical features of the laser radar laser energy adjustment device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0058] The present application provides a laser radar laser energy regulation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the laser radar laser energy regulation method in the above-mentioned embodiment one.

[0059] Reference below Figure 4 , which shows a schematic diagram of the structure of a laser radar laser energy regulating device suitable for implementing the embodiment of the present application. The laser radar laser energy regulating device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The lidar laser energy regulation device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0060] like Figure 4As shown, the laser radar laser energy regulating device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the laser radar laser energy regulating device are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the laser radar laser energy conditioning device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a laser radar laser energy conditioning device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0061] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0062] The laser radar laser energy adjustment device provided by the present application adopts the laser radar laser energy adjustment method in the above embodiment, which can solve the technical problem of how to improve the distance solution accuracy by adjusting the laser. Compared with the prior art, the beneficial effects of the laser radar laser energy adjustment device provided by the present application are the same as the beneficial effects of the laser radar laser energy adjustment method provided by the above embodiment, and the other technical features of the laser radar laser energy adjustment device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0063] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0064] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0065] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the laser radar laser energy adjustment method in the above-mentioned embodiment.

[0066] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0067] The above-mentioned computer-readable storage medium may be included in the laser radar laser energy regulation device; or it may exist independently without being assembled into the laser radar laser energy regulation device.

[0068] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the laser radar laser energy adjustment device, the laser radar laser energy adjustment device: obtains the laser emission energy of the historical ranging point, the distance solution result of the historical ranging point, and the energy value of the reflected pulse of the historical ranging point to obtain a sample matrix; Inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

[0069] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0070] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0071] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0072] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned laser radar laser energy adjustment method, and can solve the technical problem of how to improve the distance solution accuracy by adjusting the laser. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the laser radar laser energy adjustment method provided in the above-mentioned embodiment, and will not be repeated here.

[0073] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned lidar laser energy adjustment method.

[0074] The computer program product provided by this application can solve the technical problem of how to improve the accuracy of distance calculation by adjusting the laser. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the laser radar laser energy adjustment method provided by the above embodiment, which will not be repeated here.

[0075] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A laser radar laser energy adjustment method, characterized in that: The method includes: Obtain the laser emission energy of the historical ranging points, the distance solution results of the historical ranging points, and the energy values ​​of the reflected pulses of the historical ranging points to obtain a sample matrix; Inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The sample matrix is ​​input into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

2. The method according to claim 1, characterized in that The step of inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model comprises: According to the sample matrix, calculate the target value and output value; Calculating an offset between the target value and the output value; According to the offset, a laser output energy prediction model is obtained.

3. The method according to claim 2, characterized in that The step of calculating the target value and the output value according to the sample matrix includes: According to the sample matrix and the preset algorithm, the target value is obtained; According to the sample matrix, the output of the hidden layer and output layer of the neural network is calculated to obtain the output value.

4. The method according to claim 3, characterized in that The step of obtaining the target value according to the sample matrix and the preset algorithm includes: Get the lower limit and upper limit of the energy value; A target value is obtained according to the energy value of the reflected pulse at the historical ranging point, the lower limit of the energy value, and the upper limit of the energy value.

5. The method according to claim 4, characterized in that The step of obtaining the target value according to the energy value of the reflected pulse at the historical ranging point, the lower limit of the energy value and the upper limit of the energy value comprises: When the energy value of the reflected pulse at the historical ranging point is less than the lower limit of the energy value, the The lower limit of the energy value is taken as the target value; When the energy value of the reflected pulse at the historical ranging point is greater than the upper limit of the energy value, the The upper limit of the energy value is used as the target value; When the energy value of the reflected pulse at the historical ranging point is between the lower limit of the energy value and the upper limit of the energy value, the energy value of the reflected pulse at the historical ranging point is used as the target value.

6. The method according to claim 2, characterized in that The step of obtaining a laser output energy prediction model according to the offset comprises: If the offset is within the preset allowable range, the training is terminated to obtain a laser output energy prediction model; If the offset is not within the preset allowable range, the weights of the hidden layer and the output layer of the neural network are updated, and the output value is recalculated, and the process returns to the step of calculating the offset between the target value and the output value.

7. The method according to claim 6, characterized in that The step of updating the weights of the hidden layer and the output layer of the neural network includes: Calculate the error between the hidden layer and the output layer of the neural network; Calculating an error gradient based on the error; According to the error gradient, the weights of the hidden layer and output layer of the neural network are updated.

8. A laser radar laser energy adjustment device, characterized in that: The device comprises: An acquisition module acquires the laser emission energy of the ranging point, the distance solution result of the ranging point, and the energy value of the reflected pulse of the ranging point to obtain a sample matrix; A training module, inputting the sample matrix into a neural network for training to obtain a laser output energy prediction model; The calculation module inputs the sample matrix into the laser output energy prediction model for prediction to obtain the laser output energy of the next ranging point.

9. A laser radar laser energy adjustment device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the lidar laser energy adjustment method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the laser radar laser energy adjustment method as described in any one of claims 1 to 7 are implemented.

Images