CN109859156A - The processing method and processing device of abnormal frame data - Google Patents
The processing method and processing device of abnormal frame data Download PDFInfo
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Abstract
Disclose a kind of processing method and processing device of abnormal frame data.This method comprises: obtaining N frame point cloud data；The coordinate of point in every frame point cloud data comprising i direction；The coordinate for calculating separately the point in i direction in t+1 frame point cloud data obtains corresponding i sequence of differences with the difference in t frame point cloud data between the coordinate of the point in corresponding i direction；Count in i sequence of differences, difference be continuously 1 sequence number be m quantity n (m)；According to statistical result, determine whether t+1 frame point cloud data is abnormal frame data；If the t+1 frame point cloud data is abnormal frame data, t+1 frame point cloud data is replaced with kth frame point cloud data.According to the disclosure, simple process can be carried out to the abnormal frame data of radar scanning, improve data precision.
Description
Technical Field
The present disclosure relates to the technical field of radar data processing, and more particularly, to a method and an apparatus for processing abnormal frame data.
Background
The development of lidar has penetrated all industries, such as sweeping robots, automatic driving, and other specialty areas. From the perspective of software, an open-source Robot Operating System (ROS) is already very mature and has been widely applied in the field of robots, but a reliable and effective method for processing abnormal frame data in the ROS System is not provided. From the hardware perspective, for example, the SICK radar has become an industry standard, but the radar product still has the problem of abnormal frame data.
For an application scene with low requirements on the precision And the real-time performance of laser radar data, such as a sweeping robot, abnormal frame data can be converged to a certain extent in an instant positioning And Mapping (SLAM) algorithm, so that the influence is reduced. However, for application scenarios involving safety issues and other special features, such as automatic driving, the problem of abnormal frame data is particularly important if only a lidar is used.
Accordingly, the inventors have recognized a need for improvement in view of at least one of the above problems.
Disclosure of Invention
An object of the present invention is to provide a new technical solution of a method for processing abnormal frame data.
According to a first aspect of the present invention, there is provided a method for processing abnormal frame data, including:
acquiring N frames of point cloud data; each frame of point cloud data comprises coordinates of points in i directions;
respectively calculating the difference between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t frame of point cloud data to obtain corresponding i difference sequences;
counting the number n (m) of sequences with the difference value of 1 continuously in the i difference value sequences, wherein the number of the sequences is m;
determining whether the point cloud data of the t +1 th frame is abnormal frame data or not according to the statistical result;
and if the t +1 th frame of point cloud data is abnormal frame data, replacing the t +1 th frame of point cloud data with the kth frame of point cloud data.
Optionally, after acquiring N frames of point cloud data, the method further includes:
according to the formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates;
wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
Optionally, calculating a difference between coordinates of a point in i directions in the t +1 th frame of point cloud data and coordinates of a point in i directions in the t +1 th frame of point cloud data, to obtain i corresponding difference sequences, including:
according to the formulaCalculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data;
according to the formulaObtaining a corresponding difference sequence; wherein thr is a preset threshold.
Optionally, the determining whether the point cloud data of the t +1 th frame is abnormal frame data according to the statistical result includes:
if n (1) is more than or equal to 5, n (i) is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change and system abnormity, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is more than or equal to 5, and n (i) is more than or equal to 3) is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is less than 5, n (i is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change, and defining the t +1 th frame point cloud data as non-abnormal frame data;
and if n (1) < 5 and n (i is not less than 3) ═ 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
According to a second aspect of the present disclosure, there is provided an apparatus for processing abnormal frame data, including:
the acquisition module is used for acquiring N frames of point cloud data; each frame of point cloud data comprises coordinates of points in i directions;
the calculation module is used for respectively calculating the difference between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t frame of point cloud data to obtain corresponding i difference value sequences;
the counting module is used for counting the number n (m) of sequences with the difference value of 1 continuously, wherein the number of the sequences is m;
the determining module is used for determining whether the point cloud data of the t +1 th frame is abnormal frame data according to the statistical result;
and the replacing module is used for replacing the t +1 th frame of point cloud data by the kth frame of point cloud data if the t +1 th frame of point cloud data is abnormal frame data.
Optionally, the system further comprises a coordinate conversion module for converting the coordinate into a formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates;
wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
Optionally, the calculation module is specifically configured to:
according to the formulaCalculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data;
according to the formulaObtaining a corresponding difference sequence; wherein thr is a preset threshold.
Optionally, the determining module is specifically configured to:
if n (1) is more than or equal to 5, n (i) is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change and system abnormity, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is more than or equal to 5, and n (i) is more than or equal to 3) is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is less than 5, n (i is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change, and defining the t +1 th frame point cloud data as non-abnormal frame data;
and if n (1) < 5 and n (i is not less than 3) ═ 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
According to one embodiment of the disclosure, abnormal frame data scanned by the radar can be simply processed, and data accuracy is improved.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 shows a schematic flow chart of a processing method of abnormal frame data according to an embodiment of the present disclosure.
Fig. 2 shows a schematic block diagram of an abnormal frame data processing apparatus according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 shows a schematic flow chart of a processing method of abnormal frame data according to an embodiment of the present disclosure.
As shown in fig. 1, in step 1100, N frames of point cloud data are acquired; each frame of point cloud data contains coordinates of points in i directions.
It should be noted that coordinates of each point in the point cloud data obtained by scanning with the laser radar are polar coordinates, and after acquiring N frames of point cloud data, the polar coordinates need to be converted into rectangular coordinates.
In particular, according to the formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates; wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
After the coordinates are converted into rectangular coordinates, step 1200 is performed to calculate the difference between the coordinates of the point in the i directions in the t +1 th frame of point cloud data and the coordinates of the point in the i directions in the t th frame of point cloud data, so as to obtain i corresponding difference sequences.
In particular, according to the formulaAnd calculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data. According to the formulaObtaining a corresponding difference sequence; the threshold thr is a preset threshold, and needs to be set through experiments according to an actual application scenario, which is not specifically limited in this embodiment.
Step 1300, counting the number n (m) of sequences with the difference value of 1 continuously in the i difference value sequences, wherein the number of the sequences is m.
For example, the sequence of difference values is 0101011011011,the number of sequences 1 that are 1 in succession is 2, i.e., n (1) ═ 2;the number of sequences of 1 in succession is 3, that is, n (2) ═ 3.
And 1400, determining whether the point cloud data of the t +1 th frame is abnormal frame data according to the statistical result.
Specifically, if n (1) is greater than or equal to 5 and n (i) is greater than or equal to 3) is greater than or equal to 1, that is, the number of single 1 appearing in i difference value sequences is greater than or equal to 5, and the number of 1 continuous more than 3 is greater than or equal to 1, determining that the point cloud data of the t +1 th frame is radar data change caused by environmental change and system abnormality, and defining the point cloud data of the t +1 th frame as abnormal frame data.
If n (1) is more than or equal to 5, and n (i is more than or equal to 3) is 0, that is, the number of single 1 appearing in the i difference sequence is more than or equal to 5, and the number of more than 3 1 is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data.
If n (1) < 5 and n (i ≧ 3) ≥ 1, i.e. the number of single 1 appearing in i difference sequence is less than 5, and the number of 1 more than 3 is greater than or equal to 1, determining that the t +1 frame point cloud data is radar data change caused by environmental change, and defining the t +1 frame point cloud data as non-abnormal frame data.
If n (1) < 5, n (i is not less than 3) ═ 0, that is, the number of single 1 appearing in the i difference sequence is less than 5, and the number of more than 3 1 is 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
In step 1500, if the t +1 th frame of point cloud data is abnormal frame data, the t +1 th frame of point cloud data is replaced by the k frame of point cloud data.
According to the processing method of the abnormal frame data, the space-time distribution state is considered for the points in each radar scanning direction in each frame of point cloud data according to the inter-frame difference distribution, when the t +1 th frame of point cloud data is determined to be the abnormal frame data, the t +1 th frame of point cloud data is simply replaced by the k-th frame of point cloud data, the abnormal frame data can be simply processed, and the accuracy of radar scanning data is improved.
Fig. 2 shows a schematic block diagram of an abnormal frame data processing apparatus according to an embodiment of the present disclosure. As shown in fig. 2, the abnormal frame data processing apparatus 2000 of the present embodiment may include: an obtaining module 2100, a calculating module 2200, a counting module 2300, a determining module 2400, and a replacing module 2500.
The acquiring module 2100 is configured to acquire N frames of point cloud data; each frame of point cloud data comprises coordinates of points in i directions;
a calculating module 2200, configured to calculate differences between coordinates of points in i directions in the t +1 th frame of point cloud data and coordinates of points in i directions corresponding to the t +1 th frame of point cloud data, respectively, to obtain i corresponding difference sequences;
a counting module 2300, configured to count n (m) of the i difference sequences, where the number of the sequences with the difference being 1 is m;
a determining module 2400, configured to determine whether the t +1 th frame of point cloud data is abnormal frame data according to the statistical result;
and a replacing module 2500, configured to replace the t +1 th frame of point cloud data with the kth frame of point cloud data if the t +1 th frame of point cloud data is abnormal frame data.
Further, the apparatus 2000 for processing abnormal frame data of this embodiment may further include a coordinate transformation module for transforming the abnormal frame data according to a formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates; wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
In practical applications, the calculating module 2200 is specifically configured to: according to the formulaCalculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data; according to the formulaObtaining a corresponding difference sequence; wherein thr is a preset threshold.
The determining module 2400 is specifically configured to: if n (1) is more than or equal to 5, n (i) is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change and system abnormity, and defining the t +1 th frame point cloud data as abnormal frame data; if n (1) is more than or equal to 5, and n (i) is more than or equal to 3) is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data; if n (1) is less than 5, n (i is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change, and defining the t +1 th frame point cloud data as non-abnormal frame data; and if n (1) < 5 and n (i is not less than 3) ═ 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
The processing apparatus for abnormal frame data of this embodiment may be configured to execute the technical solutions of the foregoing method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. A software implementation and a hardware implementation are equivalent for the skilled person. The skilled person can choose software or hardware to implement the above described scheme as desired. Therefore, specific software or hardware is not limited herein.
The present invention may be an apparatus, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (8)
1. A method for processing abnormal frame data is characterized by comprising the following steps:
acquiring N frames of point cloud data; each frame of point cloud data comprises coordinates of points in i directions;
respectively calculating the difference between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t frame of point cloud data to obtain corresponding i difference sequences;
counting the number n (m) of sequences with the difference value of 1 continuously in the i difference value sequences, wherein the number of the sequences is m;
determining whether the point cloud data of the t +1 th frame is abnormal frame data or not according to the statistical result;
and if the t +1 th frame of point cloud data is abnormal frame data, replacing the t +1 th frame of point cloud data with the kth frame of point cloud data.
2. The method of claim 1, wherein after acquiring N frames of point cloud data, the method further comprises:
according to the formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates;
wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
3. The method of claim 1, wherein calculating differences between coordinates of points in i directions in the t +1 th frame of point cloud data and coordinates of points in corresponding i directions in the t +1 th frame of point cloud data to obtain corresponding i sequences of differences comprises:
according to the formulaCalculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data;
according to the formulaObtaining a corresponding difference sequence; wherein thr is a preset threshold.
4. The method of claim 1, wherein the determining whether the t +1 th frame point cloud data is abnormal frame data according to the statistical result comprises:
if n (1) is more than or equal to 5, n (i) is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change and system abnormity, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is more than or equal to 5, and n (i) is more than or equal to 3) is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is less than 5, n (i is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change, and defining the t +1 th frame point cloud data as non-abnormal frame data;
and if n (1) < 5 and n (i is not less than 3) ═ 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
5. An apparatus for processing abnormal frame data, comprising:
the acquisition module is used for acquiring N frames of point cloud data; each frame of point cloud data comprises coordinates of points in i directions;
the calculation module is used for respectively calculating the difference between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t frame of point cloud data to obtain corresponding i difference value sequences;
the counting module is used for counting the number n (m) of sequences with the difference value of 1 continuously, wherein the number of the sequences is m;
the determining module is used for determining whether the point cloud data of the t +1 th frame is abnormal frame data according to the statistical result;
and the replacing module is used for replacing the t +1 th frame of point cloud data by the kth frame of point cloud data if the t +1 th frame of point cloud data is abnormal frame data.
6. The apparatus of claim 5, further comprising a coordinate transformation module configured to transform the coordinate into a formulaConverting coordinates of points in i directions in N frames of point cloud data into rectangular coordinates;
wherein,is the polar coordinate of the point in the ith direction, r is the distance from the surrounding obstacles measured by the radar, the angular resolution of the radar scan.
7. The apparatus of claim 5, wherein the computing module is specifically configured to:
according to the formulaCalculating the difference value between the coordinates of the points in the i directions in the t +1 th frame of point cloud data and the coordinates of the points in the i directions corresponding to the t +1 th frame of point cloud data;
according to the formulaObtaining a corresponding difference sequence; wherein thr is a preset threshold.
8. The apparatus of claim 5, wherein the determining module is specifically configured to:
if n (1) is more than or equal to 5, n (i) is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change and system abnormity, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is more than or equal to 5, and n (i) is more than or equal to 3) is 0, determining that the t +1 th frame point cloud data is radar data change caused by system abnormality, and defining the t +1 th frame point cloud data as abnormal frame data;
if n (1) is less than 5, n (i is more than or equal to 3) is more than or equal to 1, determining that the t +1 th frame point cloud data is radar data change caused by environmental change, and defining the t +1 th frame point cloud data as non-abnormal frame data;
and if n (1) < 5 and n (i is not less than 3) ═ 0, determining that the t +1 th frame point cloud data is data with strong continuity with the t th frame point cloud data, and defining the t +1 th frame point cloud data as non-abnormal frame data.
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| WO2023220977A1 (en) * | 2022-05-18 | 2023-11-23 | Huawei Technologies Co., Ltd. | Method and device for detecting data |
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