CN115032618B

CN115032618B - Blind area repairing method and device applied to laser radar and laser radar

Info

Publication number: CN115032618B
Application number: CN202210969991.9A
Authority: CN
Inventors: 欧阳家斌; 李慧玲
Original assignee: Shenzhen Camsense Technologies Co Ltd
Current assignee: Shenzhen Huanchuang Technology Co ltd
Priority date: 2022-08-12
Filing date: 2022-08-12
Publication date: 2022-11-25
Anticipated expiration: 2042-08-12
Also published as: CN115032618A

Abstract

The embodiment of the application relates to the technical field of laser radars, and discloses a blind area repairing method and device applied to a laser radar, a laser radar and a robot. Compared with the method for repairing the blind area of the laser radar with various quality types (for example, the method for repairing the blind area of the bad radar with serious point cloud loss), the method enables the repair of the blind area to be carried out on the premise of accurate repair value, namely, the reliability of repair can be ensured. On the other hand, the blind area is repaired when the target object enters the blind area, so that errors caused by redundant repair can be avoided, the repaired measurement point cloud is accurate, and the blind area repair is accurate and effective.

Description

Blind area repairing method and device applied to laser radar and laser radar

Technical Field

The embodiment of the application relates to the field of laser radars, in particular to a blind area repairing method and device applied to the laser radars and the laser radars.

Background

With the continuous development of science and technology, laser radars are widely applied to the fields of robots, unmanned driving, unmanned vehicles and the like. Laser Detection and Ranging (LADAR) is a radar system that emits a Laser beam to detect a characteristic amount of a target, such as a position and a velocity. The lidar includes a transmitter and a receiver, the transmitter transmits a detection signal (laser) to a target, the receiver receives a signal (reflected light) reflected from the target, and the lidar compares the received signal with the transmitted signal and, after appropriate processing, obtains information about the target, such as parameters of target distance, orientation, height, speed, attitude, and even shape.

The distance measurement method used by the laser radar includes a triangular distance measurement method and a TOF (Time of Flight) method. Due to the limited width of the receiver, it can be known from the characteristics of the triangulation method that some receivers cannot receive enough echo laser reflected from a target object at a short distance, and have a near blind zone. For example, the maximum range of a lidar is M meters, and objects within n (n < M) meters from the lidar are undetectable, thus leaving a blind area of n meters.

Disclosure of Invention

The technical problem mainly solved by the embodiment of the application is to provide the blind area repairing method and device applied to the laser radar, the laser radar and the robot, the short-distance blind area can be effectively repaired, the width deficiency of a receiver is made up, and the hardware cost is saved.

In a first aspect, an embodiment of the present application provides a method for repairing a blind area applied to a laser radar, including:

acquiring calibration point cloud of a laser radar;

determining the quality type of the laser radar according to the calibrated point cloud, wherein the quality type comprises a good radar, a bad radar or a repairable radar;

if the laser radar is a repairable radar, recording the short-distance blind area distance of the laser radar;

and point cloud data generated by the repairable radar in the actual distance measurement process is obtained, and points with the measured distance smaller than or equal to the distance of the short-distance blind area in the point cloud data are repaired to obtain the repaired point cloud data.

In some embodiments, the determining the quality type of the lidar according to the calibration point cloud includes:

if the distance value of the point with the maximum brightness value in the calibrated point cloud is minimum, determining that the laser radar is a good radar;

and if the distance value of the point with the maximum brightness value in the calibration point cloud is not the minimum distance value, determining the short-distance blind area distance of the laser radar according to the calibration point cloud, and determining that the laser radar is a bad radar or a repairable radar according to the size relation between the short-distance blind area distance and a preset threshold value.

In some embodiments, the determining the near blind area distance of the lidar according to the calibration point cloud includes:

acquiring a first point with the maximum brightness from a point cloud in front of a point with the minimum brightness in the calibrated point cloud, and acquiring a second point with the maximum brightness from a point cloud behind the point with the minimum brightness in the calibrated point cloud, wherein the point clouds in the calibrated point cloud are ordered according to the sequence of the generation time from small to large;

and determining the near blind area distance as the average value of the distance of the first point and the distance of the second point.

In some embodiments, the repairing the point of which the measured distance is less than or equal to the distance of the near blind area in the point cloud data to obtain repaired point cloud data includes:

and performing distance compensation on blind area points with the measuring distance smaller than the short-distance blind area distance in the point cloud data to obtain repaired point cloud data.

In some embodiments, the distance compensation is performed on the blind area points of which the measured distance is smaller than the near-distance blind area distance in the point cloud data to obtain the repaired point cloud data, and the method includes:

any one blind area point in the point cloud data is subjected to point cloud data, a reference point which is closest to the blind area point in angle interval is obtained, and the measurement distance of the reference point is a short-distance blind area distance;

and calculating a compensation value according to the measurement distance and the angle of the reference point, and compensating the measurement distance of the blind area point by adopting the compensation value to obtain the repaired point cloud data.

In some embodiments, the method further comprises:

judging whether the repaired point cloud data is cracked;

and if the repaired point cloud data is cracked and the cracking angle is smaller than or equal to the angle threshold, adding points at the cracked position of the repaired point cloud data at equal angular distance to obtain the final repaired point cloud data.

In some embodiments, the adding points at equal angular intervals at the crack of the repaired point cloud data to obtain the final repaired point cloud data includes:

calculating a straight line segment connecting the two crack points according to the two crack points in the repaired point cloud data;

and adding points on the straight line segment at equal angular intervals to obtain the finally repaired point cloud data.

In a second aspect, an embodiment of the present application provides a device for repairing a blind area of a laser radar, including:

the acquisition module is used for acquiring calibration point cloud of the laser radar;

the first determining module is used for determining the quality type of the laser radar according to the calibration point cloud, wherein the quality type comprises a good radar, a bad radar or a repairable radar;

the recording module is used for recording the short-distance blind area distance of the laser radar if the laser radar is a repairable radar;

and the repairing module is used for acquiring point cloud data generated by the repairable radar in the actual distance measuring process, and repairing points of which the measured distance is less than or equal to the short-distance blind area distance in the point cloud data to obtain repaired point cloud data.

In a third aspect, an embodiment of the present application provides a laser radar, including:

at least one processor, and

a memory communicatively coupled to the at least one processor, wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.

In a fourth aspect, an embodiment of the present application provides a robot, including the lidar in the third aspect.

The beneficial effects of the embodiment of the application are as follows: different from the situation in the prior art, the method for repairing the blind area applied to the laser radar, provided by the embodiment of the application, obtains the calibration point cloud of the laser radar, and determines the quality type of the laser radar (the quality type includes good-quality radars, bad radars or repairable radars) according to the calibration point cloud. And if the laser radar is a repairable radar, recording the short-distance blind area distance of the laser radar. And point cloud data generated by the repairable radar in the actual distance measurement process is obtained, and points with the measured distance smaller than or equal to the distance of the short-distance blind area in the point cloud data are repaired to obtain the repaired point cloud data. In the implementation, the laser radar with the quality type of the repairable radar is screened out by analyzing the form of the calibrated point cloud, the short-distance blind area distance is calculated and recorded for the repairable radar, and in the actual ranging process, the short-distance blind area distance is compared with the measured distance of each point to serve as a condition for triggering the blind area repair of the point cloud data. Compared with the method for repairing the blind area of the laser radar with various quality types (for example, the method for repairing the blind area of the bad radar with serious point cloud loss), the method enables the repair of the blind area to be carried out on the premise of accurate repair value, namely, the reliability of repair can be ensured. On the other hand, when the measurement distance of any point in the point cloud data of the laser radar is smaller than or equal to the blind area distance (namely, when the target object enters the blind area), the blind area is repaired, so that errors caused by redundant repair can be avoided, the repaired measurement point cloud is accurate, and the blind area repair is accurate and effective. In addition, the method is adopted for blind area repair, compared with the method for improving the hardware of the laser radar receiver, the cost of the receiver can be saved, and the size and the assembly difficulty of the laser are reduced.

Drawings

One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.

Fig. 1 is a schematic application environment diagram of a blind area repairing method applied to a laser radar according to some embodiments of the present application;

FIG. 2 is a schematic diagram of a lidar triangulation method according to some embodiments of the present disclosure;

fig. 3 is a schematic flowchart of a blind area repairing method applied to a laser radar according to some embodiments of the present disclosure;

FIG. 4 is a schematic illustration of some calibration point clouds provided by some embodiments of the present application;

FIG. 5 is a schematic view of a sub-flow of step S30 of the method shown in FIG. 3;

FIG. 6 is a schematic view of a sub-flow of step S40 of the method shown in FIG. 3;

FIG. 7 is a schematic diagram of fiducial points in point cloud data in some embodiments of the present application;

fig. 8 is a schematic flowchart of a blind area repairing method applied to a laser radar according to some embodiments of the present disclosure;

FIG. 9 is a schematic view of a point cloud data repair provided by some embodiments of the present application;

FIG. 10 is a schematic view of a sub-flow chart of step S60 of the method shown in FIG. 8;

FIG. 11 is a schematic illustration of dots added in some embodiments of the present application;

FIG. 12 is a schematic view of a plurality of point cloud data according to some embodiments of the present application;

FIG. 13 is a schematic illustration of a plurality of repaired point cloud data in some embodiments of the present application;

FIG. 14 is a schematic illustration of a plurality of final repaired point cloud data in some embodiments of the present application;

FIG. 15 is a schematic structural diagram of a blind area repairing apparatus for a lidar according to some embodiments of the present disclosure;

FIG. 16 is a schematic diagram of a lidar structure in some embodiments of the present application;

fig. 17 is a schematic view of a robot according to some embodiments of the present application.

Detailed Description

The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the present application in any way. It should be noted that various changes and modifications can be made by one skilled in the art without departing from the spirit of the application. All falling within the scope of protection of the present application.

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

It should be noted that, if not conflicted, the various features of the embodiments of the present application may be combined with each other within the scope of protection of the present application. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts. Further, the terms "first," "second," "third," and the like, as used herein do not limit the order of data and execution, but merely distinguish between identical or similar items that have substantially the same function or effect.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In addition, the technical features mentioned in the embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.

Referring to fig. 1, fig. 1 is a schematic view of an application environment of a method for repairing a blind area of a laser radar according to an embodiment of the present application. As shown in fig. 1, the robot 10 is located on the ground, which may be the ground of a residential or office building or the like. The robot is located in a place comprising target objects such as desks, flowerpots, sofas and the like.

The robot is provided with a laser radar 11, the laser radar 11 scans a target object in the space to obtain point cloud data, and the distance from the target object to the laser radar is calculated based on the point cloud data. It can be understood that when the target object is close to the laser radar, the target object enters a short-distance blind area, at the moment, the laser radar also performs blind area restoration on the point cloud data, and an accurate distance is calculated based on the point cloud data after restoration. The robot can be guided to move and avoid obstacles by measuring the distance of the target object.

Wherein the robot 10 may be configured in any suitable shape to enable specific business function operations, for example, in some embodiments the robot 10 may be a SLAM system based mobile robot, such as: the robot can be a cleaning robot, a pet robot, a carrying robot, a nursing robot, a remote monitoring robot, a sweeping robot and the like. Among them, the cleaning robot includes, but is not limited to, a sweeping robot, a dust collecting robot, a mopping robot, a floor washing robot, or the like.

In some embodiments, a robot includes a main body and a drive wheel assembly, a camera unit, a sensing unit, a lidar, and a controller. The body may be generally oval, triangular, D-shaped or otherwise shaped in profile. The controller is disposed in the main body, which is a main body structure of the robot, and may be made of a corresponding shape, structure, and manufacturing material (such as hard plastic, or metal, such as aluminum, iron, etc.) according to actual needs of the robot 10, for example, the controller is disposed in a flat cylindrical shape, which is common in a sweeping robot.

The driving wheel component is arranged on the main body and used for driving the robot to move, and if the robot is a cleaning robot, the driving wheel component drives the robot to move on a surface to be cleaned. In some embodiments, the drive wheel assembly includes a left drive wheel, a right drive wheel, and an omni wheel, the left and right drive wheels being mounted to opposite sides of the body. The omniwheel is installed in the position near the front of the bottom of main part, and the omniwheel is the activity truckle, can 360 degrees rotations of level to make the robot can turn to in a flexible way. The left driving wheel, the right driving wheel and the omnidirectional wheel are arranged to form a triangle, so that the walking stability of the robot is improved.

In some embodiments, a camera unit is disposed in the body of the robot for acquiring image data and/or video data. The camera unit is in communication connection with the controller, and is used for acquiring image data and/or video data within the coverage area of the camera unit, for example: the method comprises the steps of acquiring image data and/or video data in a certain place and sending the acquired image data and/or video data to a controller. In the embodiment of the present application, the camera unit includes, but is not limited to, an infrared camera, a night vision camera, a web camera, a digital camera, a high definition camera, a 4K camera, an 8K high definition camera, and other camera devices.

In some embodiments, the sensing unit is used for collecting some motion parameters of the robot and various types of data of the environment space, and the sensing unit includes various types of suitable sensors, such as a gyroscope, an infrared sensor, a speedometer, a magnetic field meter, an accelerometer or a speedometer, and the like.

In some embodiments, a lidar 11 is communicatively coupled to the controller, the lidar 11 being disposed on the body of the robot 10, for example: the laser radar 11 is provided on a moving chassis of the body of the robot 10. The laser radar 11 is used for sensing the obstacle condition of the surrounding environment of the mobile robot 10, obtaining the distance of the surrounding objects, and sending the distance to the controller, so that the controller controls the robot to walk based on the distance of the surrounding objects. In some embodiments, the laser radar 11 includes a pulse laser radar, a continuous wave laser radar, and the like, and the mobile chassis includes a universal chassis, a bow-type mobile chassis, and the like.

In some embodiments, the controller is disposed inside the main body, and is an electronic computing core built in the robot main body, and is used for executing logical operation steps to realize intelligent control of the robot. Wherein, the controller is connected with left driving wheel, right driving wheel and omniwheel electricity respectively. The controller is used as a control core of the robot and is used for controlling the robot to walk, retreat and some business logic processing. For example: the controller is used for receiving the image data and/or the video data sent by the camera shooting unit, receiving the laser point cloud data sent by the laser radar, and constructing an environment map according to the laser point cloud data. The controller calculates laser point cloud data of the monitored area through a synchronous positioning and Mapping (SLAM) technology, namely a laser SLAM algorithm, so as to construct an environment map. In the embodiment of the application, the laser SLAM algorithm comprises Kalman filtering, particle filtering and graph optimization methods.

It will be appreciated that the controller may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a single chip, an ARM (Acorn RISC Machine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. The controller may be any conventional processor, controller, microcontroller, or state machine. A controller may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration, or one or more combinations of Micro Control Units (MCUs), field-Programmable Gate arrays (FPGAs), system-on-a-Chip (SoC).

It is understood that the robot 10 in the embodiment of the present application further includes a storage module, which includes but is not limited to: one or more of FLASH memory, NAND FLASH memory, vertical NAND FLASH memory (VNAND), NOR FLASH memory, resistive Random Access Memory (RRAM), magnetoresistive Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), spin transfer torque random access memory (STT-RAM), and the like.

It should be noted that, according to the task to be accomplished, in addition to the above functional modules, one or more other different functional modules (such as a water storage tank, a cleaning device, etc.) may be mounted on the main body of the robot and cooperate with each other to perform the corresponding task.

To facilitate understanding of the methods provided in the embodiments of the present application, the related art referred to in the embodiments of the present application is described here:

the lidar includes a transmitter, a receiver, a processor, and a rotation mechanism. The emitter is a device for emitting laser light, and may be, for example, a gas laser, a solid laser, a semiconductor laser, or a free electron laser. The receiver is a Device for receiving laser light, and may be a Charge Coupled Device (CCD), for example.

The processor is mainly responsible for controlling the emitter to emit laser, processing the laser signal received by the receiver and calculating the distance information of the target object. The Processor may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.

The rotating mechanism is a laser radar mounting framework and is used for adjusting the direction. In some embodiments, the rotation mechanism may include a rotating base that is rotated by a belt. The transmitter, the receiver and the processor are arranged on the rotating mechanism, and the rotating mechanism rotates at a stable rotating speed, so that the laser radar can scan the surrounding environment and generate real-time point cloud information.

The range finding method adopted by the laser radar includes a triangular range finding method and a TOF (Time of Flight, TOF). Referring to fig. 2, fig. 2 is a schematic diagram illustrating a triangulation method. The triangulation method is mainly characterized in that a transmitter emits a beam of laser, the laser irradiates a target object at a certain incident angle, the laser is reflected and scattered on the surface of the target object, the reflected laser is converged and imaged by a lens at another angle, and a light spot is imaged on a receiver. Since the transmitter and receiver are separated by a distance s, target objects of different distances will be imaged at different positions on the receiver, according to the optical path. As shown in fig. 2, s is a distance (i.e., a reference line) between the transmitter and the receiver, d is a distance between the transmitter and the target object, α is a heading angle, p is a vertical distance between the target object and the reference line, f is a focal length, and x is a width of the receiver for receiving the reflected laser light. And (4) making a geometric similarity triangle, wherein the distance p = f × s/x of the target object.

By adopting a triangulation distance measurement method, the point cloud data generated by the laser radar comprises the angle, the distance, the brightness and the like of each point. The angle is the angle of a light spot formed by the laser on the target object in a polar coordinate, the distance is the distance from the light spot formed by the laser on the target object to the laser radar, and the brightness is the light spot brightness.

When the target object is closer to the lidar, because the width x of the receiver is limited, at this time, the spot of the reflected laser light cannot fall in the field of view of the receiver or partially falls in the field of view of the receiver, and therefore, a near blind area exists. For example, the maximum range of a lidar is M meters, and objects within n (n < M) meters of the lidar are undetectable, thus an n-meter short-range shadow zone exists. It can be understood that in the production of the laser radars, the respective near blind zone distances n of the laser radars in the same batch are different due to assembly errors.

In some techniques for solving the problem of the blind area, which are known to the inventor of the present application, hardware is mostly improved, for example, in the solution disclosed in patent application CN201910809225.4, two receivers are provided, that is, the receiving width of the receiver is increased to compensate for the blind area. In the solution disclosed in patent application CN202111286673.4, the laser radar probe set includes a plurality of blind area compensation laser probes to compensate for the blind area. These hardware improvements increase the cost of the receiver, increase the size of the lidar, and increase assembly difficulty.

In order to solve the above problems, the embodiment of the application provides a method and a device for repairing a blind area, a laser radar and a robot, wherein the method screens out the laser radar of which the quality type is the repairable radar by analyzing the form of a calibrated point cloud, calculates and records the distance of a near-distance blind area for the repairable radar, and compares the distance of the near-distance blind area with the measured distance of each point in the actual ranging process to serve as a condition for triggering the repair of the blind area of point cloud data. Compared with the method for repairing the blind area of the laser radar with various quality types (for example, the method for repairing the blind area of the bad radar with serious point cloud loss), the method enables the repair of the blind area to be carried out on the premise of accurate repair value, namely, the reliability of repair can be ensured. On the other hand, the point cloud data with the measuring distance smaller than or equal to the distance of the near-distance blind area is repaired, namely the point corresponding to the target object entering the blind area is repaired, so that errors caused by redundant repair can be avoided, the repaired measuring point cloud is accurate, and the blind area repair is accurate and effective. In addition, the method is adopted for blind area repair, compared with the method for improving the hardware of the laser radar receiver, the cost of the receiver can be saved, and the size and the assembly difficulty of the laser are reduced.

As can be understood from the foregoing, the blind area repairing method applied to the laser radar provided in the embodiments of the present application may be implemented by various types of electronic devices with computing processing capability, for example, by the laser radar, by a controller of a robot, or by other devices with computing processing capability.

The blind area repairing method applied to the laser radar provided by the embodiment of the present application is described below with reference to an exemplary application and implementation of the laser radar provided by the embodiment of the present application. Referring to fig. 3, fig. 3 is a schematic flowchart of a blind area repairing method applied to a laser radar according to an embodiment of the present disclosure.

It is understood that the laser radar is installed in the robot, and particularly, the execution subject of the blind area repairing method applied to the laser radar is one or more processors of the laser radar.

As shown in fig. 3, the method S100 may specifically include the following steps:

s10: and acquiring calibration point cloud of the laser radar.

The calibration point cloud is obtained by calibrating the laser radar by using a calibration distance and a homogeneous target. In this embodiment, in order to obtain the minimum distance (the minimum distance that can be measured) of the lidar, the lidar is calibrated using a calibration distance and a homogeneous target. Wherein the calibration distance is a threshold value set by a person skilled in the art, and can be set according to experience or historical production data, for example, the calibration distance is 14.5cm. It will be appreciated that the same batch of lidar has an approximate minimum range, for example 1000 lidar, with a minimum range in the range of [10cm, 20cm ]. In historical production experience, if the mode of the minimum distance of the laser radar of the previous batch is 14.5cm, the mode of 14.5cm is taken as the current calibration distance.

The homogeneous target is made of the same material, for example, white cardboard. As can be understood by those skilled in the art, different materials have different absorption and scattering capabilities for laser, and if the target is heterogeneous, the brightness of the light spot is affected, that is, the brightness of the midpoint of the calibration point cloud is affected. In some embodiments, the target may be a 20cm × 20cm size white cardboard. In some embodiments, the target may be made of plastic with homogeneous color, etc., and may have a size of 20cm × 30cm square, etc. In the embodiment of the application, the target is homogeneous without any limitation.

Specifically, an object target made of the same material is arranged in front of the laser radar, the object target is placed perpendicular to a laser beam emitted by the laser radar, the laser radar is vertically aligned with the center of the object target, and the distance between the object target and the laser radar is a calibration distance, such as 14.5cm. The laser radar carries out laser scanning ranging on a target, and a processor of the laser radar calculates the coordinate, the distance, the angle and the brightness of each light spot (a light spot point formed by laser on the target) based on transmitted laser information and echo laser information. The calculation of coordinates, distance, angle and brightness of the light spot is an existing calculation method of the laser radar, and is not described in detail here.

It can be understood that the distance, angle and brightness of each spot constitute a calibration point cloud based on the continuity of the scan. Therefore, after the scanning is finished, the processor of the laser radar obtains the calibration point cloud through calculation. And the point clouds in the calibration point clouds are sorted according to the sequence of the generation time from small to large. In the calibration point cloud, the coordinates, distance, angle, and brightness of each point may be obtained. The distance is the distance from a light spot formed on the target by laser to the laser radar, the angle is the angle of the light spot formed on the target by laser in a polar coordinate system, and the coordinate is the position coordinate of the light spot formed on the target by laser in a rectangular coordinate system. The brightness is the spot brightness.

S20: and determining the quality type of the laser radar according to the calibration point cloud, wherein the quality type comprises a good radar, a bad radar or a repairable radar.

Referring to fig. 4, fig. 4 is a schematic diagram illustrating various types of calibration point clouds. It is understood that (a) in fig. 4 is a normal calibration point cloud, and it is understood that since the scanned surface of the target is a plane, each point in the calibration point cloud is on a straight line under normal conditions. Here, the normal condition means that the laser radar can measure a target, the target does not enter the minimum gauge of the laser radar, and a light spot does not enter a blind area. In the calibration point clouds in (b) to (d) in fig. 4, a protrusion appears at a position perpendicular to the light beam, and the distance and brightness of the point at the protrusion are reduced, which indicates that the light spot corresponding to the protrusion starts to enter a blind area, that is, the distance between the target corresponding to the protrusion and the laser radar is close to or smaller than the minimum distance. In the process that the light spot enters the blind area, the light spot gradually falls into the field of view of the receiver and cannot be collected by a lens of the receiver, the energy is smaller and smaller, and the brightness is also smaller and smaller; in the process that the light spot leaves the blind area, the larger the energy is, the larger the brightness is. Therefore, in the calibration point cloud shown in any one of (b) to (d) in fig. 4, the brightness and the distance of the point tend to increase and then decrease, and then increase and then decrease.

In the calibration point cloud shown in (b) to (d) in fig. 4, the protrusions become more and more serious, which indicates that the blind area occurs more and more seriously, and in the calibration point cloud shown in (d) in fig. 4, a small-range point cloud at the vertical light beam enters the blind area. It can be understood that as the target object is continuously close to the lidar, the receiver cannot collect the light spot, and the software algorithm in the lidar will set the distance and brightness of the point in this case to 0, so that the point cloud is missing and cracked. As shown in (e) of fig. 4, when the calibration point cloud is raised and cracked, that is, a plurality of points with continuous distances and brightness of 0 are present, light spots corresponding to the cracked positions completely fall outside the field of view of the receiver and cannot be collected by the receiver, so that the light spots at the raised positions are smaller in brightness and distance. In the calibration point cloud, the change rule of the point cloud from the vertical position to the two sides is approximately as follows: the distance is larger and larger, and the brightness is increased and then reduced.

In this embodiment, the obtained calibration point cloud may be one of (a) to (e) in fig. 4, so that the calibration point cloud may be analyzed to determine the quality type of the laser radar. In some embodiments, if the calibration point cloud is in the shape shown in (a) of fig. 4, it indicates that the light spots can all fall into the field of view of the receiver within the calibration distance, and the lidar is a good radar; if the calibration point cloud is in the shape shown in (b), (c) or (d) of fig. 4, it shows that the light spot is partially dropped outside the field of view of the receiver within the calibration distance, and a blind area occurs, and the laser radar is a repairable radar. If the calibrated point cloud is in the shape shown in (e) in fig. 4 and the point cloud is not serious, the lidar is also a repairable radar. For the repairable radar, the point cloud data can be repaired by adopting a software program, and the accurate point cloud data can be obtained without maintaining hardware. If the calibrated point cloud is in the shape shown in (e) in fig. 4 and the point cloud is seriously lost, the laser radar is a poor radar and cannot be repaired. For a bad radar, generally, due to the fact that a serious problem exists in assembly of components such as a receiver or a transmitter, hardware needs to be maintained, and software programs cannot be accurately repaired.

In some embodiments, for points in the calibration point cloud, a mean value of luminance and a mean value of distance are calculated for every K points, where K is an integer greater than or equal to 2. Where K is a numerical value set by one skilled in the art. In some embodiments, K may also be 4, or 5, etc. One skilled in the art can set the appropriate value of K based on the total number of points in the calibrated point cloud.

And if the distances and the brightness of the continuous N points in the calibrated point cloud are both 0, determining that the laser radar is a bad radar, wherein N is an integer larger than K. Wherein N is a deletion threshold set by a person skilled in the art, for example, N may be 30, and if there are 30 continuous points whose brightness and distance are both 0, it indicates that the deletion of the continuous point cloud is serious, and there is a serious problem in assembling components such as a receiver or a transmitter. In this case, the lidar is determined to be a bad radar if it is of no repair value.

And determining that the laser radar is a repairable radar if the distance and the brightness of m continuous points are minimum around the maximum brightness mean value, wherein m is an integer which is greater than or equal to 2 and less than N. It can be understood that, based on the principle of laser reflection and diffuse reflection, the brightness at the vertical laser beam is the maximum, and thus, the vicinity of the maximum brightness mean value is the vicinity of the vertical laser beam. If the distance of continuous m points is minimum and the brightness is minimum near the vertical laser beam, the situation that a blind area begins to appear near the vertical laser beam is shown, but serious point cloud loss does not appear, and the laser radar is a repairable radar. Wherein m is an integer greater than or equal to 2 and less than N. In some embodiments, m may be 2 or 3. And m is more than or equal to 2 and less than or equal to N, so that the interference of noise points can be effectively avoided, and the accuracy of quality diagnosis is facilitated.

And if the point with the maximum brightness mean value corresponds to the point with the minimum distance mean value, determining that the laser radar is a good radar. It can be understood that, based on the principles of laser reflection and diffuse reflection, the vertical laser beam has the maximum brightness and the minimum distance, and therefore, if the maximum brightness mean value corresponds to the minimum distance mean value, it is indicated that the measurement at the vertical laser beam is accurate and no blind area occurs.

In the implementation, the calibration point cloud is divided into a plurality of groups based on the principles of laser reflection and diffuse reflection, quality diagnosis is carried out according to the brightness mean value and the distance mean value of each group, and the quality type of the laser radar can be accurately determined.

In some embodiments, referring to fig. 5, the step S20 specifically includes:

s21: and if the distance value of the point with the maximum brightness value in the calibrated point cloud is minimum, determining that the laser radar is a good radar.

It can be understood that, based on the principles of laser reflection and diffuse reflection, the vertical laser beam has the maximum brightness and the minimum distance, so that if the distance value of the point with the maximum brightness value in the calibrated point cloud is the minimum, it is indicated that the measurement at the vertical laser beam is accurate, and no blind area occurs, it is determined that the laser radar is a good-quality radar.

S22: and if the distance value of the point with the maximum brightness value in the calibration point cloud is not the minimum distance value, determining the short-distance blind area distance of the laser radar according to the calibration point cloud, and determining that the laser radar is a bad radar or a repairable radar according to the size relation between the short-distance blind area distance and a preset threshold value.

If the distance value of the point with the maximum brightness value in the calibrated point cloud is not the minimum distance value, the vertical laser beam enters a blind area. Firstly, determining the short-distance blind area distance of the laser radar based on the calibration point cloud. Wherein, low coverage blind area distance is the minimum distance that laser radar can measure, and when the distance between target object and the laser radar was less than low coverage blind area distance, target object was located laser radar's blind area within range, and laser radar range finding is inaccurate, need restore the blind area to point data.

In order to obtain the near blind area distance (minimum distance) of the laser radar blind area, the near blind area distance may be determined according to the calibration point cloud, for example, the near blind area distance may be determined based on the distance reflected by the normal point in the calibration point cloud.

And then, comparing the distance between the near blind areas with a preset threshold value, and determining that the laser radar is a bad radar or a repairable radar. It is understood that the preset threshold is a distance threshold for determining whether the lidar can be repaired, and can be determined by those skilled in the art according to actual needs. For example, the preset threshold may be 20cm.

Specifically, if the near blind area distance is greater than a preset threshold, the quality type of the laser radar is determined to be a poor radar. It is understood that the point cloud of a poor radar is seriously lost, and generally, the assembly of components such as a receiver or a transmitter has serious problems. In this case, hardware needs to be repaired, and the software program cannot be accurately repaired, so that the laser radar is determined to be a bad radar, and the bad radar has no repair value. And if the distance of the short-distance blind area is less than or equal to a preset threshold value, determining the quality type of the laser radar as a repairable radar. For the repairable radar, the point cloud data can be repaired by adopting a software program, and the accurate point cloud data can be obtained without maintaining hardware.

In this embodiment, based on the principle of laser reflection and diffuse reflection, the brightness and the distance at the position where the vertical laser beam is the largest and the distance is the smallest, a good-product radar is determined, the distance between the near blind area and a preset threshold value is compared, and the laser radar is determined to be a bad radar or a repairable radar. The quality diagnosis is carried out in the above mode, and the quality type of the laser radar can be accurately determined. In addition, the repairable radar can be screened out, the subsequent blind area repair of the repairable radar is facilitated, the defective radar is abandoned, and compared with the method for performing the blind area repair on all the laser radars with various quality types (for example, performing the blind area repair on the defective radar with serious point cloud loss), the blind area repair is performed on the premise that the repair value is accurate, and the repair reliability can be ensured.

In some embodiments, the "determining the near blind area distance of the lidar according to the calibration point cloud" includes:

acquiring a first point with the maximum brightness from a point cloud in front of a point with the minimum brightness value in the calibration point cloud, and acquiring a second point with the maximum brightness from a point cloud in back of the point with the minimum brightness value in the calibration point cloud; and determining the near blind area distance as the average value of the distance of the first point and the distance of the second point.

It can be understood that, because the calibration point cloud of the repairable radar has a protrusion, from the scanning starting point to the scanning ending point, the brightness of the point cloud tends to increase, decrease, increase and decrease, the point with the minimum brightness value is located at or near the vertical light beam, and two points with the maximum brightness exist in the calibration point cloud. Therefore, the first point with the maximum brightness can be obtained from the point cloud in front of the vertical light beam in the calibration point cloud, and the second point with the maximum brightness can be obtained from the point cloud in back of the vertical light beam in the calibration point cloud. Based on the maximum brightness of the first point and the second point, the distance between the first point and the second point is the minimum distance which can be accurately detected by the laser radar according to the principle of laser triangulation.

Here, the accuracy of the near blind area distance can be improved by setting the average value of the distance between the first point and the second point as the near blind area distance.

S30: and if the laser radar is a repairable radar, recording the short-distance blind area distance of the laser radar.

After the laser radar obtains the short-distance blind area distance, the short-distance blind area distance can be stored in a memory of the laser radar. And in the actual ranging process, the laser radar calls the short-distance blind area distance to be used as a basis for judging whether to repair the blind area.

S40: and point cloud data generated by the repairable radar in the actual distance measurement process is obtained, and points with the measured distance smaller than or equal to the distance of the short-distance blind area in the point cloud data are repaired to obtain the repaired point cloud data.

It can be understood that the near-range blind area distance is called to compare with the measurement distance of any point in the point cloud data, when the measured distance is smaller than or equal to the near-range blind area distance, the point cloud data of the laser radar is indicated to have a blind area, namely the measurement distances of partial points are abnormal, the points with the measurement distances smaller than or equal to the near-range blind area distance in the point cloud data are repaired, the repaired point cloud data are obtained, and the distances of all the points in the repaired point cloud data are accurate.

In some embodiments, the step S40 specifically includes:

In actual measurement, when a light spot gradually falls out of the field of view of the receiver, the distance and the brightness of the point cloud are both small, and therefore, a blind area point exists, wherein the measurement distance is smaller than the short-distance blind area distance. The measured distance of the blind area point is inaccurate, and the measured distance of the blind area point needs to be compensated, so that the measured distance of each point in the repaired point cloud data is accurate, and the accuracy of the short-distance measurement of the laser radar is improved.

In some embodiments, referring to fig. 6, the step S40 specifically includes:

s41: and acquiring a reference point which is closest to the blind area point in the point cloud data at an angle interval, wherein the measurement distance of the reference point is a short-distance blind area distance.

According to the principle of laser triangulation distance measurement, when a laser radar scans the surface of a target object, the distance is the smallest when the laser is perpendicular to the target object, and the measuring distance is larger when the angles of the two sides of the perpendicular position deviate from the perpendicular position are larger. Thus, at the beginning of the point cloud data, the measured distance decreases and then increases as the angle increases. Because the measuring distance of blind area point is less than low coverage blind area distance, be the grow earlier again based on measuring the distance in some cloud data and diminish, then, the trend that the grow becomes again, the measuring distance of blind area point is less than low coverage blind area distance, consequently, has two measuring distance to be the benchmark of low coverage blind area distance. Each point in the point cloud data has information such as measuring distance, angle, brightness and the like, so that any one blind area point in the point cloud data can be measured by comparing the angle with the measuring distance, a reference point which is closest to the blind area point in angle interval is obtained, and the measuring distance of the reference point is a short-distance blind area distance.

Referring to fig. 7, for a blind area point 1# in the point cloud data, a reference point J1 that is closest to the blind area point 1# in angle interval is obtained, and a measurement distance of the reference point J1 is a near blind area distance. And for a blind area point 2# in the point cloud data, acquiring a reference point J2 which has the closest angle interval with the blind area point 2# and taking the measurement distance of the reference point J2 as the near blind area distance.

S42: and calculating a compensation value according to the measurement distance and the angle of the reference point, and compensating the measurement distance of the blind area point by adopting the compensation value to obtain the repaired point cloud data.

Because each point in the point cloud data has information such as measuring distance, angle, brightness and the like, the compensation value is calculated according to the measuring distance and angle of the reference point with the closest angle interval. The compensation value is an increased compensation value of the measured distance of the blind spot. And compensating the measurement distance of the blind area point by using a compensation value, wherein the measurement distance after the compensation of the blind area point is equal to the sum of the original measurement distance and the compensation value.

In some embodiments, the compensation value is calculated using the following equation:

y _b =a*|x _ang -P _ang |+b*|x _dis -P _dis |+c

wherein, y _b To compensate for the value, x _ang Angle of blind spot, x _dis Measured distance, P, being a blind spot _ang Is the angle of reference point, P _dis A, b, and c are weight coefficients, respectively, for the measured distance of the reference point.

The above formula is obtained by modeling through a large amount of experimental data by the inventor of the application, and specific numerical values of the weight coefficients a, b and c are fitted. It can be understood that | x _ang -P _ang L reflects the angular difference between the blind spot and the reference point, | x _dis -P _dis And | reflecting the distance difference between the blind area point and the reference point, and performing weighted summation on the angle difference and the distance difference to establish a linear equation. After fitting through a large number of experimental data, specific numerical values of the coefficients a, b and c are obtained.

It will be appreciated that the compensation value is a weighted sum of the angular difference and the distance difference between the blind point and the reference point, i.e. the further the blind point is from the corresponding reference point, the larger the compensation value. The farther from the blind area point corresponding to the reference point in the point cloud data, the smaller the measurement distance. It can be known that the compensation value is adapted to the measurement distance of the blind area point, and the smaller the measurement distance is, the larger the compensation value is, and the larger the measurement distance is, the smaller the compensation value is. Compared with the method for compensating the point cloud data for the blind area, the compensation value of each blind area point is compensated by the same compensation value, in the embodiment, the compensation value is adaptive to the measurement distance of the blind area point, the blind area point can be compensated accurately, and the repaired point cloud data is more accurate.

In some embodiments, in order to enable the repaired point cloud to be used for ranging more accurately, in some embodiments, referring to fig. 8, the method S100 further includes:

s50: and judging whether the repaired point cloud data is cracked.

S60: and if the repaired point cloud data is cracked and the cracking angle is smaller than or equal to the angle threshold, adding points at the cracked position of the repaired point cloud data at equal angular distance to obtain the final repaired point cloud data.

In this embodiment, if the repaired point cloud data has a distance of 2 continuous points and a brightness of 0, it is determined that the repaired point cloud data is cracked, and if the repaired point cloud data has no distance of 2 continuous points and a brightness of 0, it is determined that the repaired point cloud data is not cracked. It can be understood that the distance is compensated based on the point cloud data measured by the laser radar after the restoration, so that the restored point cloud data is the same as the crack opening angle of the measured point cloud data, namely if 3 points are lost in the measured point cloud data, the interval angle between the points is 2 degrees, the crack opening angle is 6 degrees, the distance compensation can not be performed due to the lost points, and therefore, 3 points are lost in the restored point cloud data, and the crack opening angle is 6 degrees.

If the number of continuously missing points in the repaired point cloud data is greater than or equal to a threshold value g, determining that the repaired point cloud data is cracked; and if the number of continuously missing points in the repaired point cloud data is less than the threshold value g, determining that the repaired point cloud data is not cracked. As shown in fig. 9, (a) in fig. 9 is a schematic view showing that the point cloud data and the repaired point cloud data are not cracked, and (b) in fig. 9 is a schematic view showing that the point cloud data and the repaired point cloud data are cracked.

It can be understood that if the point cloud data detected by the laser radar is cracked seriously, that is, the point loss is serious, there is no value of the supplementary point. In some embodiments, there may be multiple targets in front of the lidar and the angle of the cleave may be the angle of separation between two targets. Therefore, the crack degree is limited by adopting the angle threshold, if the crack angle is smaller than or equal to the angle threshold, points are added at the crack position of the repaired point cloud data at equal angular distance, and finally repaired point cloud data is obtained, so that the supplement and repair are more accurate.

In some embodiments, since the near-range blind area distance of each lidar is different, for example, lidar a with a near-range blind area distance of 16cm and lidar B with a near-range blind area distance of 14cm, when a target object 11cm ahead is detected, the point cloud data of lidar a and the point cloud data of lidar B are different in cracking degree, and the cracking degree of the point cloud data of lidar a is greater than that of the point cloud data of lidar B.

For the lidar repair to be consistent, in some embodiments, the aforementioned Angle threshold is an Angle range that is adapted to the near dead zone distance, where Angle range = α × D + β, D is the near dead zone distance, and α and β are weights. It is understood that the specific values of α and β can be set by one skilled in the art as the case may be. It can be seen that the larger the short-distance blind area distance of the laser radar, the larger the angle threshold value, and the smaller the short-distance blind area distance, the smaller the angle threshold value. Continuing with the example above, for lidar A and lidar B, the angle threshold of lidar B is greater than the angle threshold of lidar B.

In the embodiment, the angle threshold of the laser radar is adapted to the distance of the short-distance blind area, namely, the distance between the supplement point and the short-distance blind area is adapted, so that the condition that the radar with supplement value is mistakenly removed or the radar without supplement value is mistakenly supplemented due to the fixed angle threshold can be effectively prevented, and the finally repaired point cloud data is more accurate.

In some embodiments, referring to fig. 10, the step S60 specifically includes:

s61: and calculating a straight line segment connecting the two split points according to the two split points in the repaired point cloud data.

S62: and adding points on the straight line segment at equal angular intervals to obtain the finally repaired point cloud data.

And the crack point is a point adjacent to the crack in the repaired point cloud data. The distance and brightness of the point on one side of the cracking point are both 0, and the distance and brightness of the point on the other side are normal.

The repaired point cloud data comprises information such as coordinates, angles, distances and brightness of all points, so that the information such as the coordinates, the angles, the distances and the brightness of the split points L1 and L2 can be obtained from the repaired point cloud data according to the characteristics of the split points.

Specifically, the cleavage point L1 coordinate (x) ₁ ,y ₁ ) L2 coordinate of cleavage point (x) ₂ ,y ₂ ) Solving a linear equation y = k × x + b, wherein k = (y) ₂ -y ₁ )/(x ₂ -x ₁ ). As shown in fig. 11, a straight line passes through the cleavage point L1 and the cleavage point L2, and on a straight line segment between the cleavage point L1 and the cleavage point L2, points are added at equal angular intervals, and for example, if the angle of the cleavage point L1 is 2 °, the angle of the cleavage point L2 is 10 °, and one point is added at every 2 °, 3 points are added between L1 and L2. It is to be understood that the separation angle may be the separation angle of each point in the point cloud data.

Therefore, the finally repaired point cloud data is complete and free of missing points, and the distance of the target object can be determined accurately in the follow-up process.

In an actual scene, there may be a plurality of target objects entering the near blind area, please refer to fig. 12, after the laser radar rotates clockwise to scan for a circle, there are 3 target objects entering the near blind area. Because intervals exist among all target objects, based on the laser reflection principle, the farther laser is spread, the weaker the energy is, and therefore, at the intervals, the laser is attenuated and has no reflection information and no point cloud. Thus, the laser radar can distinguish point cloud data of different target objects. In some embodiments, whether the point cloud data belong to the same target object may be determined by setting a second angle threshold.

It is understood that when a plurality of target objects enter the near-field blind area of the lidar, the point cloud data of each target object may be one of (a) to (e) in fig. 4. Referring to fig. 12, fig. 12 is a schematic diagram of a plurality of point cloud data acquired by a laser radar. After the laser radar acquires the point cloud data, the near-distance blind area distance stored in the laser radar is called to be compared with the measurement distance of each point in the point cloud data. And for one point cloud data, when the measurement distance of any point in the point cloud data is smaller than or equal to the near blind area distance, performing blind area repair on the point with the measurement distance smaller than or equal to the near blind area distance in the point cloud data to obtain repaired point cloud data.

Specifically, distance compensation is performed on blind area points of which the measured distance is smaller than the short-distance blind area distance in the point cloud data, and the repaired point cloud data is obtained. In some embodiments, a reference point which is closest to the blind point in the point cloud data at an angle interval is obtained, and the measurement distance of the reference point is a near blind area distance. And calculating a compensation value according to the measurement distance and the angle of the reference point, and compensating the measurement distance of the blind area point by adopting the compensation value to obtain the repaired point cloud data. It is to be understood that each of the plurality of point cloud data is repaired in the above-described manner, as shown in fig. 13, and fig. 13 is the repaired plurality of point cloud data.

And then, judging whether the repaired point cloud data is cracked. And if the repaired point cloud data is cracked and the cracking angle is smaller than or equal to the angle threshold, adding points at the cracked position of the repaired point cloud data at equal angular distance to obtain the final repaired point cloud data. It is understood that the broken point cloud data in the repaired plurality of point cloud data is added with points in the above-described manner, as shown in fig. 14, and fig. 14 is the finally repaired plurality of point cloud data.

To sum up, the method for repairing the blind area applied to the laser radar obtains the calibration point cloud of the laser radar (the calibration point cloud is obtained by calibrating the laser radar by using the calibration distance and the homogeneous target), and determines the quality type of the laser radar (the quality type includes a non-defective radar, a defective radar or a repairable radar) according to the calibration point cloud. And if the laser radar is a repairable radar, recording the short-distance blind area distance of the laser radar. And acquiring point cloud data generated by the repairable radar in the actual distance measurement process, and when the measurement distance of any point in the point cloud data is smaller than or equal to the short-distance blind area distance, performing blind area repair on the point with the measurement distance smaller than or equal to the short-distance blind area distance in the point cloud data to obtain repaired point cloud data. In the implementation, the laser radar with the quality type of the repairable radar is screened out by analyzing the form of the calibrated point cloud, the short-distance blind area distance is calculated and recorded for the repairable radar, and in the actual ranging process, the short-distance blind area distance is compared with the measured distance of each point to serve as a condition for triggering the blind area repair of the point cloud data. Compared with the method for repairing the blind area of the laser radar with various quality types (for example, the method for repairing the blind area of the bad radar with serious point cloud loss), the method enables the repair of the blind area to be carried out on the premise of accurate repair value, namely, the reliability of repair can be ensured. On the other hand, when the measurement distance of any point in the point cloud data of the laser radar is smaller than or equal to the blind area distance (namely, when the target object enters the blind area), the blind area is repaired, so that errors caused by redundant repair can be avoided, the repaired measurement point cloud is accurate, and the blind area repair is accurate and effective. In addition, the method is adopted for blind area repair, compared with the method for improving the hardware of the laser radar receiver, the cost of the receiver can be saved, and the size and the assembly difficulty of the laser device are reduced.

Referring to fig. 15, fig. 15 is a schematic structural diagram of a blind area repairing apparatus for a laser radar according to an embodiment of the present disclosure. The laser radar blind area repairing device is applied to the laser radar, and particularly applied to one or more processors of the laser radar.

As shown in fig. 15, the blind area repairing apparatus 200 includes: the device comprises an acquisition module 201, a first determination module 202, a recording module 203 and a repair module 204.

The obtaining module 202 is configured to obtain a calibration point cloud of the laser radar. The first determining module 202 is configured to determine a quality type of the lidar according to the calibrated point cloud, where the quality type includes a good radar, a bad radar, or a repairable radar. And the recording module 203 is used for recording the short-distance blind area distance of the laser radar if the laser radar is a repairable radar. And the repairing module 204 is configured to acquire point cloud data generated by the repairable radar in an actual distance measurement process, and perform blind area repairing on a point of the point cloud data, where a measured distance is less than or equal to a short-distance blind area distance, to obtain repaired point cloud data.

In the embodiment of the present application, the laser radar blind area repairing device may also be built by hardware devices, for example, the laser radar blind area repairing device may be built by one or more than two chips, and the chips may work in coordination with each other to complete the laser radar blind area repairing method described in each of the embodiments. For another example, the blind area repairing apparatus for laser radar may also be constructed by various logic devices, such as a general processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a single chip, an ARM (Acorn RISC Machine) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

The blind area repairing device of the laser radar in the embodiment of the application can be a device with an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, and embodiments of the present application are not limited specifically.

The blind area repairing device for the laser radar provided by the embodiment of the application can realize the processes realized in fig. 3-14, and is not repeated here for avoiding repetition.

It should be noted that the blind area repairing device for the laser radar can execute the blind area repairing method applied to the laser radar provided by the embodiment of the present application, and has the corresponding functional modules and beneficial effects of the executing method. For technical details that are not described in detail in the embodiment of the apparatus for repairing a blind area of a laser radar, reference may be made to the method for repairing a blind area applied to a laser radar provided in the embodiment of the present application.

Fig. 16 shows a schematic diagram of a hardware structure of a laser radar according to an embodiment of the present application;

as shown in fig. 16, lidar 300 includes at least one processor 301 and a memory 302 communicatively coupled (a bus connection, one processor is an example in fig. 16).

Processor 301 is configured to provide computing and control capabilities to control lidar 300 to perform corresponding tasks, for example, to control lidar 300 to perform a lidar repair method in any of the above method embodiments, where the repair method includes: and acquiring calibration point cloud of the laser radar (the calibration point cloud is obtained by calibrating the laser radar by using a calibration distance and a homogeneous target), and determining the quality type of the laser radar (the quality type comprises a good-quality radar, a bad radar or a repairable radar) according to the calibration point cloud. And if the laser radar is a repairable radar, recording the short-distance blind area distance of the laser radar. And when the measurement distance of any point in the point cloud data is smaller than or equal to the short-distance blind area distance, repairing the point with the measurement distance smaller than or equal to the short-distance blind area distance in the point cloud data to obtain the repaired point cloud data.

The method comprises the steps of screening out the laser radar with the quality type of the repairable radar by analyzing and calibrating the form of point cloud, calculating the repairable radar and recording the short-distance blind area distance, and comparing the short-distance blind area distance with the measuring distance of each point in the actual distance measuring process to serve as a condition for triggering the blind area repair of the point cloud data. Compared with the method for repairing the blind area of the laser radar with various quality types (for example, the method for repairing the blind area of the bad radar with serious point cloud loss), the method enables the repair of the blind area to be carried out on the premise of accurate repair value, namely, the reliability of repair can be ensured. On the other hand, when the measuring distance of any point in the point cloud data of the laser radar is smaller than or equal to the blind area distance (namely, when the target object enters the blind area), the blind area is repaired, so that errors caused by redundant repair can be avoided, the repaired measuring point cloud is accurate, and the blind area repair is accurate and effective. In addition, the method is adopted for blind area repair, compared with the method for improving the hardware of the laser radar receiver, the cost of the receiver can be saved, and the size and the assembly difficulty of the laser are reduced.

Processor 301 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a hardware chip, or any combination thereof; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.

The memory 302 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the laser radar repair method in the embodiments of the present application. The processor 301 may implement the laser radar repairing method in any one of the above method embodiments by running the non-transitory software program, the instructions, and the modules stored in the memory 302, that is, the processes implemented in fig. 3 to fig. 14 can be implemented, and details are not described here to avoid repetition.

In particular, memory 302 may include Volatile Memory (VM), such as Random Access Memory (RAM); the memory 302 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD), or other non-transitory solid-state memory device; the memory 302 may also comprise a combination of memories of the kind described above.

In the present embodiment, the memory 302 may also include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

In this embodiment, the laser radar 300 may further include a wired or wireless network interface, a keyboard, an input/output interface, and other components to facilitate input and output, and the laser radar 300 may further include other components for implementing functions of the device, which is not described herein again.

Referring to fig. 17, fig. 17 is a schematic structural diagram of a robot according to an embodiment of the present disclosure;

as shown in fig. 17, the robot 400 includes: laser radar 300 and controller 401, wherein, laser radar 300 is connected with this controller 401 in communication, this controller 401 is used for sending the range finding instruction to laser radar 300 to make laser radar 300 carry out the range finding. It will be appreciated that the ranging command may be sent by an external terminal to robot 400, which is forwarded by controller 401 to laser radar 300. The external terminal may be a fixed terminal or a mobile terminal, for example: electronic devices such as computers, mobile phones, tablets, etc., are not limited herein.

It should be noted that, for the specific hardware structure of the robot, reference may be made to the contents mentioned in the above embodiments, and details are not described herein again.

Embodiments of the present application also provide a computer-readable storage medium, such as a memory, including program code, which is executable by a processor to perform the blind spot repairing method applied to lidar in the above embodiments. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CDROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

Embodiments of the present application also provide a computer program product including one or more program codes stored in a computer readable storage medium. The processor of the electronic device reads the program code from the computer-readable storage medium, and the processor executes the program code to perform the method steps of the blind spot repairing method applied to the laser radar provided in the above-described embodiments.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by hardware associated with program code, and the program may be stored in a computer readable storage medium, where the above mentioned storage medium may be a read-only memory, a magnetic or optical disk, etc.

It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; within the context of the present application, where technical features in the above embodiments or in different embodiments can also be combined, the steps can be implemented in any order and there are many other variations of the different aspects of the present application as described above, which are not provided in detail for the sake of brevity; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A blind area repairing method applied to a laser radar is characterized by comprising the following steps:

obtaining calibration point cloud of a laser radar, wherein the calibration point cloud is obtained by calibrating the laser radar by using a calibration distance and a homogeneous target;

determining the quality type of the laser radar according to the calibration point cloud, wherein the quality type comprises a good radar, a bad radar or a repairable radar;

and point cloud data generated by the repairable radar in the actual distance measurement process is obtained, and points with the measured distance smaller than or equal to the short-distance blind area distance in the point cloud data are repaired to obtain the repaired point cloud data.

2. The method of claim 1, wherein determining the quality type of the lidar from the calibration point cloud comprises:

if the distance value of the point with the maximum brightness value in the calibration point cloud is minimum, determining that the laser radar is a good radar;

and if the distance value of the point with the maximum brightness value in the calibration point cloud is not the minimum distance value, determining the near-range blind area distance of the laser radar according to the calibration point cloud, and determining that the laser radar is a bad radar or a repairable radar according to the size relation between the near-range blind area distance and a preset threshold value.

3. The method of claim 2, wherein determining the near blind spot distance of the lidar from the calibration point cloud comprises:

acquiring a first point with the maximum brightness from a point cloud in front of a point with the minimum brightness value in the calibration point cloud, and acquiring a second point with the maximum brightness from a point cloud behind the point with the minimum brightness value in the calibration point cloud, wherein the point clouds in the calibration point cloud are ordered according to the sequence of the generation time from small to large;

and determining the near blind area distance as the mean value of the distance of the first point and the distance of the second point.

4. The method of claim 1, wherein the repairing points of the point cloud data whose measured distance is less than or equal to the near blind area distance to obtain repaired point cloud data comprises:

and performing distance compensation on blind area points of which the measured distance is smaller than the short-distance blind area distance in the point cloud data to obtain the repaired point cloud data.

5. The method of claim 4, wherein the distance compensation of the blind area point of the point cloud data whose measured distance is smaller than the near blind area distance to obtain the repaired point cloud data comprises:

for any blind area point in the point cloud data, obtaining a reference point which is closest to the blind area point in angular interval, wherein the measurement distance of the reference point is the near blind area distance;

6. The method of claim 4, further comprising:

judging whether the repaired point cloud data is cracked or not;

and if the repaired point cloud data is cracked and the cracking angle is smaller than or equal to the angle threshold, adding points at the cracked position of the repaired point cloud data in an equiangular distance manner to obtain the final repaired point cloud data.

7. The method of claim 6, wherein adding points at equal angular intervals at the crack of the repaired point cloud data to obtain final repaired point cloud data comprises:

and adding points on the straight line segment at equal angular distance to obtain the finally repaired point cloud data.

8. A blind area repairing device for a laser radar is characterized by comprising:

the acquisition module is used for acquiring calibration point cloud of the laser radar, wherein the calibration point cloud is obtained by calibrating the laser radar by adopting a calibration distance and a homogeneous target;

and the repairing module is used for acquiring point cloud data generated by the repairable radar in the actual distance measuring process, and performing blind area repairing on points of which the measured distance is less than or equal to the short-distance blind area distance in the point cloud data to obtain repaired point cloud data.

9. A lidar characterized by comprising:

at least one processor, and

a memory communicatively coupled to the at least one processor, wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A robot, characterized by comprising a lidar according to claim 9.