Lidar high and low gain data fusion method and device and electronic equipment

Abstract

Embodiments of the present application disclose a laser radar high-low gain data fusion method and device and electronic equipment, and relate to the technical field of laser radar detection, which is used for realizing high-low gain data smooth connection in a transition zone where both gain measurement values are effective. In the laser radar high-low gain data fusion method, the laser radar adopts a high-low gain detection scheme to obtain high gain data and low gain data, and the method comprises the following steps: acquiring the high gain data and the low gain data; assigning a first weighting coefficient to the low gain data and a second weighting coefficient to the high gain data, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1; and adding the weighted high gain data and the low gain data to obtain fused measurement distance.

Description

Technical Field

[0001] This invention relates to the field of lidar detection technology, and in particular to a lidar high and low gain data fusion method, apparatus, electronic device and storage medium. Background Technology

[0002] LiDAR needs to detect targets over a wide range, from hundreds of meters away to within a few meters. This requires the LiDAR detection system to have a large dynamic range, which can be achieved by designing two receiving gains, high and low.

[0003] LiDAR systems can differentiate between high-gain and low-gain receivers by using optical beam splitting, setting receiver circuit gain parameters, or a combination of both. When both gains are operating simultaneously, low-gain data is primarily used when the echo power is strong at close range, while high-gain data is primarily used when the echo power is weak at long range.

[0004] When employing a high-gain and low-gain detection scheme, the question arises of how to select the detection data—specifically, whether to use high-gain or low-gain data for a single ranging measurement. Regardless of whether the distinction is based on distance or pulse width, the ranging results from the two gain methods will differ, resulting in a data step at the boundary. This manifests as layering or discontinuity in the point cloud. For example, using low-gain data for distances less than 20 meters and high-gain data for distances greater than 20 meters will result in a data step at the 20-meter boundary. The same issue exists when distinguishing based on pulse width. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus, electronic device and storage medium for high and low gain data fusion of lidar, which can achieve smooth connection of high and low gain data in the transition region where both gain measurements are effective.

[0006] In a first aspect, embodiments of the present invention provide a method for fusing high- and low-gain data from a lidar system, wherein the lidar employs a high- and low-gain detection scheme to obtain high-gain data and low-gain data, and the method includes:

[0007] Acquire the high-gain data and low-gain data;

[0008] A first weighting coefficient is assigned to the low-gain data, and a second weighting coefficient is assigned to the high-gain data, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1;

[0009] The weighted high-gain data and low-gain data are added together to obtain the fused measurement distance.

[0010] In conjunction with the first aspect, in one embodiment of the first aspect, the first weighting coefficient gradually decreases as the high-gain data increases.

[0011] In conjunction with the first aspect, in another embodiment of the first aspect, assigning a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data includes:

[0012] The difference between the low-gain pulse width and the set minimum effective pulse width is obtained to get the first difference value;

[0013] The difference between the high-gain distance and the set minimum high-gain distance is obtained to get the second difference value;

[0014] The first weighting coefficient is calculated based on the first difference and the second difference.

[0015] In conjunction with the first aspect, in another embodiment of the first aspect, the step of calculating the first weighting coefficient based on the first difference and the second difference includes:

[0016] Assign a fusion coefficient to the second difference;

[0017] The sum of the first difference and the second difference after applying the fusion coefficient is calculated to obtain the first sum;

[0018] The first weighting coefficient is obtained by calculating the ratio of the first difference to the first sum.

[0019] Secondly, embodiments of the present invention provide a high- and low-gain data fusion device for a lidar, wherein the lidar employs a high- and low-gain detection scheme to obtain high-gain data and low-gain data, and the device includes:

[0020] The acquisition module is used to acquire the high-gain data and the low-gain data;

[0021] A weighting module is used to assign a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1;

[0022] The addition module is used to add the weighted high-gain data and low-gain data to obtain the fused measurement distance.

[0023] In conjunction with the second aspect, in one embodiment of the second aspect, the first weighting coefficient gradually decreases as the high-gain data increases.

[0024] In conjunction with the second aspect, in another embodiment of the second aspect, the weighting module includes:

[0025] The first acquisition unit is used to acquire the difference between the low-gain pulse width and the set minimum effective pulse width to obtain the first difference value;

[0026] The second acquisition unit is used to acquire the difference between the high gain distance and the set minimum high gain distance, and obtain the second difference value;

[0027] The calculation unit is used to calculate the first weighting coefficient based on the first difference and the second difference.

[0028] In conjunction with the second aspect, in another embodiment of the second aspect, the computing unit includes:

[0029] The assignment subunit is used to assign a fusion coefficient to the second difference;

[0030] The first calculation subunit is used to calculate the sum of the first difference and the second difference after applying the fusion coefficient to obtain the first summation;

[0031] The second calculation subunit is used to calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient.

[0032] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is disposed within the space enclosed by the housing, and the processor and the memory are disposed on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for executing any of the aforementioned methods.

[0033] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement any of the methods described above.

[0034] This invention provides a method, apparatus, electronic device, and storage medium for fusing high and low gain data from a lidar system. The lidar employs a high and low gain detection scheme to obtain high-gain and low-gain data. First, the high-gain and low-gain data are acquired. Then, a first weighting coefficient is assigned to the low-gain data, and a second weighting coefficient is assigned to the high-gain data, wherein the sum of the first and second weighting coefficients is 1. Finally, the weighted high-gain and low-gain data are added together to obtain the fused measurement distance. This invention, by fusing high-gain and low-gain data, can smoothly transition the data in the high-low gain transition region, avoiding data abrupt changes. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram illustrating the distance measurement capabilities of lidar in existing technologies.

[0037] Figure 2 This is a flowchart illustrating an embodiment of the high- and low-gain data fusion method for lidar according to the present invention.

[0038] Figure 3 This is a trend chart of the first weighting coefficient in an embodiment of the high and low gain data fusion method for lidar of the present invention.

[0039] Figure 4 This is another trend diagram of the first weighting coefficient in an embodiment of the high and low gain data fusion method for lidar of the present invention.

[0040] Figure 5 This is a schematic diagram of the measured distance of a lidar after employing the high and low gain data fusion method of the lidar of the present invention.

[0041] Figure 6 This is a flowchart of the preprocessing process before fusion in an embodiment of the high and low gain data fusion method for lidar of the present invention.

[0042] Figure 7 This is a schematic diagram of an embodiment of the high and low gain data fusion device for lidar according to the present invention.

[0043] Figure 8 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed Implementation

[0044] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0045] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0046] In existing technologies, when lidar employs a high-low gain detection scheme, it obtains both high-gain and low-gain measurement values. These high-gain and low-gain values ​​exhibit a certain deviation in the transition region. If high-low gain data fusion is not performed, and high-gain or low-gain output is selected solely based on distance (as mentioned in the background technology), the effect is as follows: Figure 1 As shown, there is a data step at the critical distance.

[0047] On one hand, embodiments of the present invention provide a method for fusing high and low gain data from a lidar system. The lidar employs a high and low gain detection scheme to obtain high-gain data and low-gain data, such as... Figure 2 As shown, the method of this embodiment includes:

[0048] Step 101: Obtain the high-gain data and low-gain data;

[0049] Step 102: Assign a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1;

[0050] Step 103: Add the weighted high-gain data and low-gain data to obtain the fused measurement distance.

[0051] The specific calculation method for steps 102-103 above can be as follows:

[0052] d = k * d0 + (1 - k) * d1

[0053] Where d is the fused distance, d0 is the low-gain distance (i.e., low-gain data), d1 is the high-gain distance (i.e., high-gain data), k is the fusion coefficient (i.e., the first weighting coefficient), and the preferred value range is (0, 1), and (1-k) is the second weighting coefficient.

[0054] Considering the significant numerical differences between high-gain and low-gain data, preferably, the first weighting coefficient k gradually decreases as the high-gain data increases, thus giving high weight to low-gain data at close range and high-gain data at long range, so as to make the fused measurement distance more accurate.

[0055] As an optional embodiment, assigning a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data (step 102) may include:

[0056] Step 1021: Obtain the difference between the low-gain pulse width and the set minimum effective pulse width to get the first difference value;

[0057] Step 1022: Obtain the difference between the high-gain distance and the set minimum high-gain distance to get the second difference value;

[0058] Step 1023: Calculate the first weighting coefficient based on the first difference and the second difference.

[0059] The first difference indirectly reflects the size of low-gain data, and the second difference indirectly reflects the size of high-gain data. Based on these two differences, the first weighting coefficient k can be calculated relatively accurately.

[0060] Furthermore, the step of calculating the first weighting coefficient based on the first difference and the second difference (step 1023) may include:

[0061] Step 10231: Assign a fusion coefficient to the second difference;

[0062] Step 10232: Calculate the sum of the first difference and the second difference after assigning the fusion coefficient to obtain the first sum;

[0063] Step 10233: Calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient.

[0064] For steps 10231-10233 above, in specific implementation, the first weighting coefficient k can be calculated as follows:

[0065]

[0066] Where w0 is the low-gain pulse width, w_min is the set minimum effective pulse width, d_min is the set minimum high-gain distance, i.e., the high-gain blind zone value, and a is the fusion coefficient, which is used to adjust the effect of the high-low gain transition zone, i.e., whether the fusion distance is closer to the low-gain distance or closer to the high-gain distance.

[0067] In the above formula, the numerator w0-w_min is the first difference, the denominator d1-d_min is the second difference, and the entire denominator is the first sum.

[0068] The smaller 'a' is, the closer 'k' is to 1, and the closer the fusion distance is to the low-gain distance; the larger 'a' is, the closer 'k' is to 0, and the closer the fusion distance is to the high-gain distance. Figure 3 and Figure 4 The figures show the trends of k as a function of w0 and d1 when a = 0.3 and a = 3, respectively.

[0069] The high- and low-gain data fusion method for lidar in this invention involves obtaining high-gain and low-gain data using a high- and low-gain detection scheme. First, the high-gain and low-gain data are acquired. Then, a first weighting coefficient is assigned to the low-gain data, and a second weighting coefficient is assigned to the high-gain data, wherein the sum of the first and second weighting coefficients is 1. Finally, the weighted high-gain and low-gain data are added together to obtain the fused measurement distance. This invention, by fusing high-gain and low-gain data, can smoothly transition the data in the high- and low-gain transition region, avoiding data abrupt changes.

[0070] The present invention will be illustrated below with a specific embodiment. Figure 2 The technical solutions of the illustrated method embodiments will be described in detail.

[0071] Assuming the high-gain dead zone is 8 meters (d_min = 8) and the low-gain minimum effective pulse width is 5 ns (w_min = 5), then the formula for calculating k is as follows:

[0072]

[0073] The distance fusion formula is as follows:

[0074] d = k * d0 + (1 - k) * d1

[0075] The fusion distance is calculated using the above method, and the result is as follows: Figure 5 As shown, in the high-low gain transition region, the fusion distance lies between the high-gain and low-gain measured values. Approaching the high-gain dead zone, the fusion distance equals the low-gain measured value; approaching the maximum low-gain range (pulse width equal to 5 ns), the fusion distance equals the high-gain measured value. Furthermore, when a = 0.3, the fusion distance is closer to the low-gain measured value, and when a = 3, the fusion distance is closer to the high-gain measured value.

[0076] It is understood that the embodiments of the present invention mainly address the problem of data fusion in the high-low gain transition region. When only high-gain data is valid in a measurement, or only low-gain data is valid, or both are valid but the data deviation is too large, fusion is not required. Fusion is only necessary when both high-gain and low-gain data are valid and their deviation is less than a given threshold. Figure 2 The fusion process shown in the embodiment. For details of the preprocessing steps before fusion, please refer to [link / reference needed]. Figure 6 .

[0077] On the other hand, embodiments of the present invention provide a high- and low-gain data fusion device for lidar, wherein the lidar employs a high- and low-gain detection scheme to obtain high-gain data and low-gain data, such as... Figure 7 As shown, the device includes:

[0078] Acquisition module 11 is used to acquire the high-gain data and low-gain data;

[0079] The weighting module 12 is used to assign a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1;

[0080] The addition module 13 is used to add the weighted high-gain data and low-gain data to obtain the fused measurement distance.

[0081] The apparatus of this embodiment can be used to perform Figure 2 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0082] Preferably, the first weighting coefficient gradually decreases as the high-gain data increases.

[0083] Preferably, the weighting module 12 may include:

[0084] The first acquisition unit is used to acquire the difference between the low-gain pulse width and the set minimum effective pulse width to obtain the first difference value;

[0085] The second acquisition unit is used to acquire the difference between the high gain distance and the set minimum high gain distance, and obtain the second difference value;

[0086] The calculation unit is used to calculate the first weighting coefficient based on the first difference and the second difference.

[0087] Preferably, the computing unit may include:

[0088] The assignment subunit is used to assign a fusion coefficient to the second difference;

[0089] The first calculation subunit is used to calculate the sum of the first difference and the second difference after applying the fusion coefficient to obtain the first summation;

[0090] The second calculation subunit is used to calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient.

[0091] This invention also provides an electronic device. Figure 8 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention, which can realize the present invention. Figure 2 The process of the illustrated embodiment is as follows: Figure 8As shown, the above-mentioned electronic device may include: a housing 41, a processor 42, a memory 43, a circuit board 44, and a power supply circuit 45, wherein the circuit board 44 is disposed inside the space enclosed by the housing 41, and the processor 42 and the memory 43 are disposed on the circuit board 44; the power supply circuit 45 is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory 43 is used to store executable program code; the processor 42 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 43, for executing the method described in any of the foregoing method embodiments.

[0092] For details on the specific execution process of the above steps by processor 42, and the steps further executed by processor 42 through running executable program code, please refer to the present invention. Figure 2 The description of the illustrated embodiments will not be repeated here.

[0093] This electronic device exists in various forms, including but not limited to:

[0094] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0095] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0096] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0097] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0098] (5) Other electronic devices with data interaction functions.

[0099] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in any of the above method embodiments.

[0100] Embodiments of the present invention also provide an application program that is executed to implement the method provided in any method embodiment of the present invention.

[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments. For ease of description, the above devices are described by dividing them into various functional units / modules. Of course, in implementing this invention, the functions of each unit / module can be implemented in one or more software and / or hardware.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for fusing high and low gain data from a lidar system, characterized in that, The lidar employs a high-low gain detection scheme to obtain high-gain data and low-gain data, the method comprising: Acquire the high-gain data and low-gain data; Assigning a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data includes: The difference between the low-gain pulse width and the set minimum effective pulse width is obtained to get the first difference value; The difference between the high-gain distance and the set minimum high-gain distance is obtained to get the second difference value; The first weighting coefficient is calculated based on the first difference and the second difference, including: Assign a fusion coefficient to the second difference; The sum of the first difference and the second difference after applying the fusion coefficient is calculated to obtain the first sum; Calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient; The sum of the first weighting coefficient and the second weighting coefficient is 1; The weighted high-gain data and low-gain data are added together to obtain the fused measurement distance.

2. The method according to claim 1, characterized in that, The first weighting coefficient gradually decreases as the high-gain data increases.

3. A high- and low-gain data fusion device for lidar, characterized in that, The lidar employs a high-low gain detection scheme to obtain high-gain data and low-gain data. The device includes: The acquisition module is used to acquire the high-gain data and the low-gain data; The weighting module is used to assign a first weighting coefficient to the low-gain data and a second weighting coefficient to the high-gain data, including: obtaining the difference between the low-gain pulse width and the set minimum effective pulse width to obtain a first difference value; The difference between the high-gain distance and the set minimum high-gain distance is obtained to get the second difference value; The first weighting coefficient is calculated based on the first difference and the second difference, including: Assign a fusion coefficient to the second difference; The sum of the first difference and the second difference after applying the fusion coefficient is calculated to obtain the first sum; Calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient; The first weighting coefficient gradually decreases as the high-gain data increases, wherein the sum of the first weighting coefficient and the second weighting coefficient is 1; The addition module is used to add the weighted high-gain data and low-gain data to obtain the fused measurement distance; The weighting module includes: The first acquisition unit is used to acquire the difference between the low-gain pulse width and the set minimum effective pulse width to obtain the first difference value; The second acquisition unit is used to acquire the difference between the high gain distance and the set minimum high gain distance to obtain a second difference value; the calculation unit is used to calculate the first weighting coefficient based on the first difference value and the second difference value.

4. The apparatus according to claim 3, characterized in that, The computing unit includes: The assignment subunit is used to assign a fusion coefficient to the second difference; The first calculation subunit is used to calculate the sum of the first difference and the second difference after applying the fusion coefficient to obtain the first summation; The second calculation subunit is used to calculate the ratio of the first difference to the first sum to obtain the first weighting coefficient.

5. An electronic device, characterized in that, The electronic device includes: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is disposed inside the space enclosed by the housing, and the processor and the memory are disposed on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the electronic device; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for executing the method described in claim 1 or 2 above.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method of claim 1 or 2.

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