CN115079128B - Method and device for distortion removal of laser radar point cloud data and robot - Google Patents
Method and device for distortion removal of laser radar point cloud data and robot Download PDFInfo
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Abstract
The application relates to a method, a device and a robot for distortion removal of laser radar point cloud data. The method corrects the distance of the distortion point cloud generated by the support column, and smoothes the region to be processed obtained by extending the angle of the support column position, thereby improving the accuracy of point cloud ranging.
Description
Technical Field
The application relates to a laser radar ranging and positioning technology, in particular to a method and a device for distortion removal of laser radar point cloud data and a robot.
Background
At present, with the improvement of the sweeper technology and the increase of functions, the sweeping robot gradually replaces manual sweeping, and the sweeper can accurately judge obstacles and benefit from an ultrasonic bionic detection technology and a laser radar positioning technology. The radar that uses on the robot of sweeping the floor is a can utilize time of flight range finding principle or the laser rangefinder of trigonometry principle, generally, the radar is including embedded and external mode in the position of the robot of sweeping the floor, in order can effectually to avoid laser radar to bump at the during operation, the extrusion, the action that damages radar installations such as fish tail, in the outside of radar, corresponding safety cover or casing lid can be installed, when radar emission laser beam, because the existence of support column on safety cover or the casing lid, make the facula of laser can be sheltered from or cut apart by the support column, lead to the point cloud to cause breach or layering in support column angle department during laser rangefinder, if the safety cover is the totally transparent enclosed structure, when having mar or spot on the safety cover, also can shelter from the facula of laser, lead to the point cloud to appear breach or layering phenomenon, the deviation appears when leading to the fact the robot of sweeping the floor to range finding, influence the robot of sweeping the floor and normally travel.
Disclosure of Invention
The technical problem that this application embodiment mainly solved is how to optimize the support column and shelter from and lead to appearing the distortion point cloud in the laser radar point cloud data to improve the precision of point cloud when the range finding.
In a first aspect, an embodiment of the present application provides a method for distortion removal of laser radar point cloud data, where the method includes:
obtaining calibration point cloud of a laser radar, and determining a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, wherein the support column is arranged at the periphery of the laser radar;
and acquiring point cloud data generated during laser radar ranging, determining a region to be processed in the point cloud data based on the first angle value, and performing distance correction and smoothing on the region to be processed to obtain target point cloud data.
In some embodiments, the performing distance correction and smoothing on the to-be-processed region to obtain target point cloud data includes:
determining one or more reference points from the region to be processed;
determining one or more sub-areas in the area to be processed according to the one or more reference points;
performing distance correction on the point cloud of each subarea in the to-be-processed area to obtain a corrected to-be-processed area;
and smoothing the point cloud of the corrected region to be processed to obtain target point cloud data.
In some embodiments, after the determining the first angle value of the support column relative to the lidar, the method further comprises:
extending the first angle value by a first preset angle to obtain an angle area to be processed;
determining one or more reference angle values from the angle area to be processed;
the determining one or more reference angle values from the angle area to be processed comprises: judging whether a point cloud is generated at the supporting column; if the point cloud is generated at the supporting column, determining the angle value of the point cloud closest to the laser radar in the angle area to be processed as a reference angle value; if no point cloud is generated at the supporting column, calculating the absolute value of the distance difference between every two adjacent points in the angle area to be processed, and determining one or more reference angle values according to the absolute value of the distance difference.
In some embodiments, the determining one or more sub-regions in the region to be processed according to the one or more reference points includes: selecting a reference point, expanding preset number of point clouds on the left and right of the selected reference point respectively, or expanding a second preset angle on the left and right of the selected reference point respectively to obtain sub-areas corresponding to the selected reference point, wherein the point clouds in the area to be processed are ordered according to the generation time.
In some embodiments, the distance correcting the point cloud in each sub-region of the region to be processed includes: selecting a sub-area from the area to be processed, and taking the distance value of a first point in the sub-area as a distance correction value of each point on the left side of a reference point in the selected sub-area; taking the distance value of the last point in the sub-area as the distance correction value of each point to the right of the reference point in the selected sub-area; and taking the distance value of the first point or the distance value of the last point in the sub-area as the distance correction value of the datum point in the sub-area.
In some embodiments, the smoothing the point cloud of the corrected region to be processed includes: sequentially taking each point in the corrected region to be processed as an initial point, performing mean value smoothing on continuous m points, and outputting a smoothing result; the mean smoothing processing on the continuous m points comprises the following steps: and taking the average value of the continuous m points as the distance value of the m points after the starting point is smoothed.
In some embodiments, detecting whether a jumping point exists in the m point cloud; and if yes, carrying out mean value smoothing treatment on the point clouds except the jumping points in the m point clouds.
In some embodiments, the detecting whether there are jumping points in the m point clouds comprises: calculating a first difference absolute value of the distance between every two point clouds in the m point clouds; recording the minimum value of the absolute value of the first difference as t; calculating the average value of the distance values of the two point clouds corresponding to the t and recording the average value as r; when the t is smaller than a first threshold value, calculating a second difference absolute value between the distance value of other point clouds except two point clouds corresponding to the t in the m point clouds and the r; judging whether the absolute value of the second difference value is larger than a second threshold value or not; and if so, determining the point cloud corresponding to the second difference absolute value as the jumping point.
In some embodiments, when t is greater than or equal to a first threshold, then smoothing is not performed on the m point clouds.
In a second aspect, an embodiment of the present application provides a laser radar point cloud data distortion removing apparatus, which includes:
the acquisition module is used for acquiring calibration point cloud of the laser radar, and determining a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, wherein the support column is arranged around the laser radar;
and the processing module is used for acquiring point cloud data generated in the laser radar ranging process, determining a region to be processed in the point cloud data based on the first angle value, and performing distance correction and smoothing on the region to be processed to obtain target point cloud data.
In a third aspect, an embodiment of the present application provides a laser radar, including:
the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for laser radar point cloud data distortion removal according to the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a robot, which includes the above laser radar.
Different from the situation of the related technology, the method, the device and the robot for laser radar point cloud data distortion removal provided by the embodiment of the application have the beneficial effects that: the method comprises the steps of obtaining calibration point cloud of a laser radar, and determining a first angle value of a supporting column relative to the laser radar according to position information of supporting columns around the laser radar and the calibration point cloud; and acquiring point cloud data generated during laser radar ranging, determining a region to be processed in the point cloud data based on the first angle value, and performing distance correction and smoothing on the region to be processed to obtain target point cloud data. The method corrects the distance of the distortion point cloud generated by the support column, and smoothes the region to be processed obtained by extending the angle of the support column position, thereby improving the accuracy of point cloud ranging.
Drawings
One or more embodiments are illustrated in corresponding drawings which are not intended to be limiting, in which elements having the same reference number designation may be referred to as similar elements throughout the drawings, unless otherwise specified, and in which the drawings are not to scale.
Fig. 1 is a schematic structural diagram of a laser radar embedded in a sweeping robot according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a laser radar placed outside a sweeping robot according to an embodiment of the present disclosure;
FIG. 3 is a schematic view of a scene where no point cloud is formed at a support column according to an embodiment of the present disclosure;
FIG. 4 is a schematic view of a scene where a point cloud is formed at a supporting post according to an embodiment of the present disclosure;
fig. 5 is a schematic flowchart of a laser radar point cloud data distortion removal method according to an embodiment of the present disclosure;
FIG. 6 is a schematic view of another scenario provided by an embodiment of the present application for generating a point cloud at a support column;
FIG. 7 is a schematic view of another scenario provided by an embodiment of the present application in which no point cloud is generated at a support column;
fig. 8 is a scene schematic diagram of a point cloud gap occurrence hierarchy provided in the embodiment of the present application;
fig. 9 is a schematic view of a scene in which point cloud gaps are processed hierarchically by using other methods in the embodiment of the present application shown in fig. 8;
FIG. 10 is a schematic view of the scene after point cloud gaps and nearby distorted point clouds in FIG. 6 are corrected according to the embodiment of the present application;
FIG. 11 is a schematic view of a scene after point cloud gaps and nearby distorted point clouds in FIG. 7 are corrected according to an embodiment of the present disclosure;
fig. 12 is a schematic structural diagram of a laser radar point cloud data distortion removal device according to an embodiment of the present disclosure;
fig. 13 is a schematic structural diagram of a laser radar according to an embodiment of the present application.
Detailed Description
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 conflicting, the individual 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 the device diagrams, with logical sequences shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than the block divisions in the device diagrams, or the flowcharts.
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 application.
Laser radar has embedded and external for sweeping robot's position is common, specifically, as shown in fig. 1, fig. 1 provides a laser radar is embedded in the inside schematic structure of robot that sweeps the floor, laser radar 13 is embedded in robot 11 that sweeps the floor, 11 outside organic casings of robot that sweeps the floor are used for protecting the robot internal installation that sweeps the floor, support column 12 is used for supporting the casing lid of laser radar 13 top, the quantity of support column 12 is decided because of different radar appearances, for example, there are one or two support columns 12 in 13 left place ahead of laser radar, one or two support columns 12 in 13 right place ahead of laser radar, support column 12 is connected and can be regarded as a whole 14 about laser radar 13.
As shown in fig. 2, fig. 2 provides a schematic structural diagram of a laser radar placed outside a sweeping robot, a laser radar 23 is placed on an upper layer of a casing cover of a sweeping robot 21, a protective cover is installed on the periphery of the laser radar 23, support pillars 22 can be used for supporting the protective cover on the periphery of the laser radar 23, the number of the support pillars 22 depends on different radar shapes, for example, 2 support pillars, 3 support pillars or 4 support pillars are symmetrically arranged around the laser radar 23, and the support pillars 22 around the laser radar 23 are connected together to form a whole 24, that is, the protective cover of the laser radar 23.
When the robot of sweeping the floor normally worked, laser radar the place ahead only had the circumstances such as a support column, support column are thinner, transparent safety cover exists smudge, mar, and laser radar produces the point cloud on the support column at the during operation more difficultly, because the sheltering from of support column for the point cloud that generates has the breach. As shown in fig. 3, fig. 3 provides a schematic view of a scene in which no point cloud is formed at a supporting column, which is a view of a scene in which the laser radar in fig. 2 is located outside the sweeping robot, when the laser radar 34 normally works, a normal target point cloud 32 can be output, a distortion point cloud 31 deviating from a normal point cloud track is generated due to shielding of the supporting column 33, the distance measurement of the laser radar is inaccurate due to the generated distortion point cloud 31, and a gap exists in the generated target point cloud 32 after being shielded by the supporting column 33.
When the robot of sweeping the floor normally worked, there were two or more than two support columns laser radar the place ahead, and the support column thickness that the robot of sweeping the floor used is different to and laser radar rotates the position that the facula was hit at the support column at every turn and is different, consequently, along with laser radar's rotation, probabilistic formation point cloud in support column department. As shown in fig. 4, fig. 4 provides a scene diagram of a point cloud formed at a supporting column, which is a scene diagram of a situation that the laser radar in fig. 2 is located outside the sweeping robot, when the laser radar 44 works normally, a normal target point cloud 42 can be output, a distorted point cloud 41 deviating from a normal point cloud track is generated due to shielding of the supporting column 43, the supporting column 43 is thick, the laser radar 44 generates a distorted point cloud at the supporting column 43 in a rotating process, and a gap exists in the generated target point cloud 42 after being shielded by the supporting column 43. Under a general condition, when the time-of-flight ranging is adopted, point clouds are generated on the supporting columns, and it should be noted that when the time-of-flight ranging is adopted, each supporting column corresponds to one point cloud notch. When adopting the triangulation method range finding, if the distance of laser radar and support column is in the blind area distance, support column department can not produce some clouds, if the distance of laser radar and support column is not in the blind area distance, then support column probabilistic production point clouds, need explain that, when adopting the triangulation method range finding, every support column corresponds two some cloud breachs.
It should be noted that, due to the difference in the distance position and angle of the target object to be measured with respect to the laser radar, the size of the generated gap is also different, but since the position of the support column with respect to the laser radar is fixed, the angle of the formed point cloud gap is not changed.
When the sweeping robot works normally, due to the fact that the support columns are shielded, distorted point clouds are generated, and a gap is formed in the point clouds generated by the laser radar, and the laser radar ranging deviation is caused. Specifically, the following is further described with reference to specific examples.
The embodiment of the application provides a laser radar point cloud data distortion removing method. Referring to fig. 5, the method specifically includes the following steps:
step S1: the method comprises the steps of obtaining calibration point cloud of a laser radar, and determining a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, wherein the support column is arranged at the periphery of the laser radar.
The calibration point cloud is formed by placing a target in front of the supporting column, the color of the target can be white, the laser radar emits laser, the laser is reflected when meeting the target, and the receiving end of the laser radar receives the point cloud generated by reflected light to form the calibration point cloud.
The support column is the supporter that is used for supporting laser radar the place ahead casing lid, and the existence of support column can make the some cloud that produce in the laser radar working process breach appears, and the distortion appears in the some cloud near the breach simultaneously, except the support column, still including smudge, mar etc. on the transparent machine box lid also can make some cloud this kind of situations appear.
The position information of the support column includes information such as the distance and direction of the support column relative to the laser radar, and the distance and direction between the support columns.
Adaptively acquiring a first angle value of the support column relative to the lidar includes: 1. if point clouds are displayed in the distance range of the cover of the machine shell, determining a first angle value of the support column relative to the laser radar according to the point clouds in the distance range of the cover of the machine shell; 2. and if the point clouds are not displayed in the distance range of the cover of the machine shell, placing a target in front of the supporting column, and determining a first angle value of the supporting column relative to the laser radar according to two point clouds corresponding to the maximum value in the absolute difference value of every two point clouds.
The non-adaptively acquiring a first angle value of the support column relative to the lidar comprises: the position of the laser radar in the sweeper or the electronic equipment is fixed, and the first angle value information and the distance information of the support column relative to the laser radar can be measured. Because, according to the mechanical assembly, the position of the device in the sweeper or the electronic equipment is fixed once the device is assembled, the value can be measured before the device leaves the factory, and the device can be directly used subsequently according to the requirement.
In this embodiment, after the determining the first angle value of the supporting column with respect to the lidar, the step S1 further includes:
extending the first angle value by a first preset angle to obtain an angle area to be processed, and determining one or more reference angle values from the angle area to be processed.
First angle value is the angle value of support column for laser radar, first angle value can not include the distortion point cloud angle of a plurality of breach departments is whole, consequently, can extend the same first angle of predetermineeing about first angle value, the difference according to laser radar's rotational speed and angle resolution, the setting value of first angle of predetermineeing also differs, the breach scope of the setting standard for can containing the support column formation of first angle of predetermineeing, make the regional point cloud breach that contains this department support column and cause of angle of pending, this department point cloud breach is all the breach that single support column formed (including the laser radar transmitting terminal by sheltering from the breach that forms, the receiving terminal is sheltered from the breach that forms).
It should be noted that, due to the influence of the number and the placement positions of the support columns in the casing cover or the protective cover, there may be a plurality of formed angle regions to be processed, for example, there are two support columns with a large angle difference, there are two angle regions to be processed, there are three support columns with a large angle difference, and if there are two support columns on the tangent line of the laser radar, the two support columns only correspond to one angle region to be processed.
One or more reference angle values exist in the angle area to be processed.
The step S1 only needs to be executed once, after the execution is finished, the obtained first angle value and the reference angle value are stored in the memory, and the first angle value and the reference angle value are directly called in the subsequent actual distance measurement process so as to be used for processing subsequent point cloud data.
Step S2: and acquiring point cloud data generated during laser radar ranging, determining a region to be processed in the point cloud data based on the first angle value, and performing distance correction and smoothing on the region to be processed to obtain target point cloud data.
In this embodiment, the performing distance correction and smoothing on the to-be-processed area to obtain target point cloud data includes: determining one or more reference points from the region to be processed; determining one or more sub-areas in the area to be processed according to the one or more reference points; performing distance correction on the point cloud of each subarea in the to-be-processed area to obtain a corrected to-be-processed area; and smoothing the point cloud of the corrected region to be processed to obtain target point cloud data.
In the point cloud data, a point corresponding to the reference angle value is a reference point, because one or more reference angle values exist, one or more reference points exist in the point cloud data, and one reference point corresponds to one sub-region, so that the to-be-processed region comprises one or more sub-regions. Because the influence of support column in every subregion, can have the point cloud that deviates from normal orbit in the point cloud that forms, in order to can more accurate range finding, need carry out the distance correction to the point cloud that deviates from normal orbit in every subregion.
The point clouds in all sub-areas of the corrected to-be-processed area are not in good transition after distance correction, so that the point clouds in the area need to be smoothed, and the smoothed point cloud data can improve the ranging accuracy of the robot.
In the embodiment of the application, the point cloud data at the gap and nearby are optimized by the method, so that the distance measurement deviation caused by the point cloud gap and nearby distorted point clouds is improved and the distance measurement precision of the point clouds is improved during laser radar distance measurement.
If the point cloud is determined to be generated at the supporting column according to the point cloud condition, a first angle value of the supporting column relative to the laser radar is obtained, if the point cloud is determined not to be generated at the supporting column according to the point cloud condition, a target is placed right in front of the supporting column, a difference absolute value of the adjacent point cloud from the laser radar is obtained, whether the difference absolute value is larger than or equal to a first preset threshold value or not is judged, and if the difference absolute value is larger than or equal to the first preset threshold value, the first angle value of the supporting column relative to the laser radar is determined according to the adjacent point cloud corresponding to the difference absolute value larger than or equal to the first preset threshold value.
The thickness degree of support column has decided the radar laser can produce the point cloud on the support column to a certain extent, therefore, detect the point cloud condition that support column department that laser radar corresponds produced, judge whether support column department produces the point cloud, if support column department has produced the point cloud, then acquire the first angle value of support column for laser radar, because the distance for laser radar is fixed after the support column assembly, through setting for the distance threshold value, the distance of laser radar to the support column is referred to in this distance threshold value's settlement, alright discernment support column produces the point cloud, and then confirm the first angle value of support column for laser radar. It should be noted that the point cloud gap and the nearby distorted point cloud are caused by the supporting column, and the angle value of the point cloud gap relative to the lidar can be determined according to the angle value of the supporting column relative to the lidar. For example, according to a time-of-flight ranging method, when a point cloud is generated at a support column, a sweeping robot is started to run for a plurality of seconds, the distance from the support column to a laser radar on the sweeping robot is fixed, the distance from the center of the laser radar to the support column is measured to be 8cm, a distance threshold value is set to be 8cm, the point cloud formed by the support column can be identified, and then a first angle value of the support column relative to the laser radar is determined.
When the distance measurement is carried out by aiming at the triangulation method, if only one supporting column exists in front of a laser radar, the supporting column is thin, point clouds are difficult to generate at the supporting column, namely, the point clouds are not generated at the supporting column, in order to determine a first angle value of the supporting column relative to the laser radar, a target is placed at a preset position right in front of the supporting column, or the laser radar is opposite to a plane of a straight wall, the absolute value of the difference value between two adjacent point clouds from the laser radar is calculated, whether the absolute value of the difference value is larger than or equal to a first preset threshold value or not is judged, only one group of two adjacent point clouds are required to be generated according to the set value of the first preset threshold value, if the absolute value of the difference value is larger than or equal to the first preset threshold value, the two adjacent point clouds corresponding to the absolute value of the difference value are used as a gap angle shielded by the supporting column, and if the angle is continuous, the point cloud closest to the supporting column is the first angle value relative to the laser radar, and if the angle value is discontinuous, the average value of the point clouds is taken as the first angle value relative to the supporting column relative to the laser radar.
The two methods can self-adaptively determine the first angle value of the support column relative to the laser radar, and it should be noted that the first angle value of the support column relative to the laser radar can also be set by itself, because the position of the support column relative to the laser radar is fixed and unchanged after the protective cover or the casing cover of the laser radar is installed, the first angle value of the support column relative to the laser radar can be obtained, and the angle value of the point cloud notch relative to the laser radar can be further obtained.
In some embodiments, the determining one or more reference angle values from the angle region to be processed includes:
step S31: judging whether a point cloud is generated at the supporting column;
step S32: if the point cloud is generated at the supporting column, determining the angle value of the point cloud closest to the laser radar in the angle area to be processed as a reference angle value;
step S33: if no point cloud is generated at the supporting column, calculating the absolute value of the distance difference between every two adjacent points in the angle area to be processed, and determining one or more reference angle values according to the absolute value of the distance difference.
And acquiring point clouds in the angle area to be processed, detecting the condition of the point clouds generated at the supporting column, judging whether the point clouds are generated at the supporting column, and if the point clouds are generated at the supporting column, determining the angle value of the point cloud closest to the laser radar in the angle area to be processed as a reference angle value.
If no point cloud is generated at the supporting column, calculating the difference absolute value of the cloud distance between adjacent points in the angle area to be processed and the laser radar, judging whether the difference absolute value is greater than or equal to a second preset threshold, if so, determining a reference angle value according to the adjacent point cloud corresponding to the difference absolute value, and taking the angle value of the point cloud closest to the laser radar in the two adjacent point clouds as the reference angle value. The first preset threshold is larger than the second preset threshold. One or more reference angle values are in the angle area to be processed.
In some embodiments, determining one or more sub-regions of the region to be processed based on the one or more reference points comprises: selecting a reference point, expanding preset number of point clouds on the left and right of the selected reference point respectively, or expanding a second preset angle on the left and right of the selected reference point respectively to obtain sub-areas corresponding to the selected reference point, wherein the point clouds in the area to be processed are ordered according to the generation time.
The selection of the point clouds of the preset number adjacent to the left and right of the datum point is related to the thickness degree of the supporting columns and the number of the point clouds in the same direction, the specific set number can be determined according to the point cloud state displayed in real time, and the value of the preset number n is 3 to 5 under general conditions. And with the reference point as a reference, respectively extending a second preset angle from left to right or respectively extending a preset number of point clouds from left to right, wherein the setting of the second preset angle/preset number of point clouds needs to cover a point cloud gap and a distortion point where the reference point is located.
In some embodiments, the distance correcting the point cloud in each sub-region of the region to be processed includes:
step S41: selecting a sub-area from the area to be processed, and taking the distance value of a first point in the sub-area as the distance correction value of each point on the left side of the reference point in the selected sub-area;
step S42: taking the distance value of the last point in the sub-area as the distance correction value of each point to the right of the reference point in the selected sub-area;
step S43: and taking the distance value of the first point or the distance value of the last point in the sub-area as the distance correction value of the datum point in the sub-area.
In some embodiments, referring to fig. 6, fig. 6 provides another schematic view of a scene where point clouds are generated at a support column, where a reference point P1 is a point cloud closest to the laser radar 51, n is equal to 4, the 4 th point cloud on the left of the reference point P1 is a point cloud a, the 4 th point cloud on the right of the reference point P1 is a point cloud b, the areas where the point clouds a and b are located are a sub-area, the point cloud a is the first point cloud in the sub-area, and the point cloud b is the last point cloud in the sub-area. Calculating the distance between the center of the laser radar 51 and the point cloud a, recording as a first correction distance, calculating 4 points on the left side of the reference point P1, calculating the distance between other point clouds except the point cloud a and the center of the laser radar 51, recording as a left point cloud distance, acquiring the angle value of each point cloud in the 4 point clouds on the left side of the reference point, calculating the distance between the center of the laser radar 51 and the point cloud b, recording as a second correction distance, calculating 4 points on the right side of the reference point P1, calculating the distance between other point clouds except the point cloud b and the center of the laser radar 51, recording as a right point cloud distance, acquiring the angle value of each point cloud in the 4 point clouds on the right side of the reference point, adjusting the positions of the 4 point clouds on the left side of the reference point according to the first correction distance, the angle value of each point cloud and the left point cloud distance, and adjusting the positions of the 4 point clouds on the right side of the reference point according to the right end point distance, the angle value of each point cloud and the right end point distance, and adjusting the positions of the right point cloud, so that the positions of the 4 point clouds on the right end point and the distance, and adjusting the positions of the 4 points on the right end point cloud of the right end point, and completing the distance. And calculating the point cloud distance between the reference point P1 and the laser radar 51, and performing distance correction on the reference point P1 according to the point cloud distance, the reference angle value and the first correction distance, or the point cloud distance, the reference angle value and the right second correction distance.
In some embodiments, referring to fig. 7, fig. 7 provides another schematic view of a scene where no point cloud is generated at the support column, where the reference point P2 is the point cloud closest to the laser radar 61, n is equal to 4, the 4 th point cloud on the left of the reference point P2 is the c point cloud, the 4 th point cloud on the right of the reference point P2 is the d point cloud, the areas where the c point cloud and the d point cloud are located are a sub-area, the c point cloud is the first point cloud in the sub-area, and the d point cloud is the last point cloud in the sub-area. Calculating the distance between the center of the laser radar 61 and the c point cloud, recording as a first correction distance, calculating the distance between other point clouds except the c point cloud and the center of the laser radar 61 in 4 points on the left side of the reference point P2, recording as a left point cloud distance, acquiring the angle value of each point cloud in the 4 point clouds on the left side of the reference point, calculating the distance between the center of the laser radar 61 and the d point cloud, recording as a second correction distance, calculating the distance between other point clouds except the d point cloud and the center of the laser radar 61 in 4 points on the right side of the reference point P2, recording as a right point cloud distance, acquiring the angle value of each point cloud in the 4 point clouds on the right side of the reference point, adjusting the positions of the 4 point clouds on the left side of the reference point according to the first correction distance, the angle value of each point cloud and the left point cloud distance, and similarly adjusting the positions of the 4 point clouds on the right side of the reference point according to the second correction distance, the angle value of each point cloud and the right point cloud distance, so that the 4 points on the right side of the reference point P2 complete distance correction. And calculating the point cloud distance between the reference point P2 and the laser radar 61, and performing distance correction on the reference point P2 according to the point cloud distance, the reference angle value and the first correction distance, or the point cloud distance, the reference angle value and the right second correction distance.
The method has the advantages that when the point cloud gaps are layered on different objects in front and back, the point cloud at the point cloud gaps is close to the edges of the objects, and the distance measurement of the point cloud is not greatly influenced, if other methods are used, for example, please refer to fig. 8 and 9, fig. 8 provides a scene schematic diagram of the layered situation of the point cloud gaps, fig. 9 is a scene schematic diagram of the layered processing of the point cloud gaps by adopting other methods in fig. 8, as shown in fig. 8 and 9, the point cloud e and the point cloud f in fig. 8 are connected, and the point cloud at the gaps is corrected to the point cloud e and the point cloud f, because the point cloud at the support columns is just at the two layered objects, the point cloud data is processed by the method, the connection points e and f are directly taken to be a linear equation, and the point cloud at the gaps are placed on the lines of the linear equation according to the original angles of the point clouds, point clouds between the two layered different objects can generate point cloud information, so that the sweeping robot cannot pass through the point cloud actually, and the different layered objects are empty in the middle of the layered objects are not generated.
In some embodiments, referring to fig. 6 and fig. 10 simultaneously, fig. 10 is a scene schematic diagram after correction of the point cloud gap and the nearby distorted point cloud of fig. 6, calculating a difference between the first correction distance and the left point cloud distance, adjusting the positions of the 4 point clouds on the left side of the reference point P1 according to the difference according to the angle value of each of the 4 point clouds on the left side of the reference point P1 and the transmitting direction of the laser radar, wherein it is understood that the first correction distance is greater than the left point cloud distance before adjustment, after adjustment, the distances between the 4 point clouds on the left side of the reference point P1 and the center of the laser radar 51 are equal, calculating a difference between the second correction distance and the right point cloud distance, and adjusting the positions of the 4 points on the right side of the reference point P1 according to the angle value of each of the 4 point clouds on the right side of the reference point P1 and the transmitting direction of the laser radar, and it is understood that the second correction distance is less than the right point cloud distance, and after adjustment, the distances between the 4 point clouds on the right side of the reference point P1 and the center of the laser radar 51 are equal.
In some embodiments, referring to fig. 7 and fig. 11 simultaneously, fig. 11 is a schematic view of a scene after a point cloud gap and nearby distorted point clouds of fig. 7 are corrected, a difference between a first correction distance and a left point cloud distance is calculated, positions of 4 point clouds on the left side of a reference point P2 are adjusted according to the difference according to an angle value of each point cloud in the 4 point clouds on the left side of the reference point P2 and along a laser radar emission direction, it can be understood that the first correction distance is greater than the left point cloud distance before adjustment, after adjustment, distances between the 4 point clouds on the left side of the reference point P2 and a center of the laser radar 61 are equal, a difference between a second correction distance and a right point cloud distance is calculated, a distance between the 4 point clouds on the right side of the reference point P2 and a center of the laser radar is deviated from the laser radar emission direction according to the angle value of each point cloud in the 4 point clouds on the right side of the reference point P2, and it can be understood that the second correction distance is smaller than the right point cloud distance, and after adjustment, distances between the 4 point clouds on the right side of the reference point P2 and the center of the laser radar 61 are equal.
In some embodiments, referring to fig. 7 and 11 simultaneously, the point cloud distance between the reference point P2 and the center of the lidar 61 is calculated, the point cloud distance is recorded as L, the difference between L and the first correction distance or the difference between L and the second correction distance is calculated, the position of the reference point P2 is adjusted along the transmitting direction of the lidar 61, and the difference between L and the first correction distance or the second correction distance, and the reference point P2 is added to the point cloud gap.
In some embodiments, the smoothing the point cloud of the corrected region to be processed includes: sequentially taking each point in the corrected region to be processed as an initial point, carrying out mean value smoothing on continuous m points, and outputting a smoothing result; the mean smoothing processing on the continuous m points comprises the following steps: and taking the average value of the continuous m points as the distance value of the m points after the starting point is smoothed. The selected m point clouds are m point clouds arranged in the corrected to-be-processed area according to the time sequence.
And performing mean smoothing on the m point clouds, and outputting a smoothing result. And removing the first point cloud in the m point clouds according to the arrangement sequence to obtain m-1 point clouds, adding one point cloud arranged behind the tail point cloud in the m point clouds to the m-1 point clouds to obtain new m point clouds, performing distance mean on the new m point clouds to serve as a distance value after the starting point in the m points is smoothed, circulating the process of performing distance mean smoothing on the m point clouds until the number of the current point clouds subjected to distance mean smoothing is smaller than m, finishing the distance mean smoothing operation of the point clouds, and outputting the point clouds subjected to distance mean smoothing according to the time sequence of smoothing.
In some embodiments, detecting whether a jumping point exists in the m point clouds; and if yes, carrying out mean value smoothing treatment on the point clouds except the jumping points in the m point clouds.
In some embodiments, the detecting whether there are jumping points in the m point clouds comprises: calculating a first difference absolute value of the distance between every two point clouds in the m point clouds; recording the minimum value of the absolute value of the first difference as t; calculating the average value of the distance values of the two point clouds corresponding to the t and recording the average value as r; when the t is smaller than a first threshold value, calculating a second difference absolute value between the distance value of the other point clouds except the two point clouds corresponding to the t in the m point clouds and the r; judging whether the absolute value of the second difference value is larger than a second threshold value; and if so, determining the point cloud corresponding to the second difference absolute value as the jumping point.
In some embodiments, when the t is greater than or equal to a first threshold, the m point clouds are not smoothed.
In some embodiments, m point clouds in the corrected region to be processed are selected, a first difference absolute value between every two point clouds in the m point clouds and the laser radar is calculated, the minimum value of the first difference absolute value is selected and recorded as t, the average value of the two point clouds corresponding to the t and the laser radar is calculated and recorded as r, the magnitude of the t and the first threshold is judged, if the t is larger than or equal to the first threshold, the m point clouds are not subjected to smoothing processing, if the t is smaller than the first preset threshold, second difference absolute values between the distances between the other point clouds in the m point clouds except the two points corresponding to the t and the laser radar and the magnitude of the r are calculated, the magnitude of the second difference absolute value and the second threshold is judged, if the second difference absolute value is larger than or equal to the second threshold, the point clouds corresponding to the second difference absolute value are not subjected to smoothing processing, the point clouds corresponding to the second difference absolute value are removed from the m point clouds, and the rest point clouds are subjected to smoothing processing.
The first threshold value is set to prevent the point cloud gap from layering on different objects, the first threshold value can be 20cm, and by combining practical conditions, for example, a bucket is placed in the center of a hall, and a wall is arranged behind the bucket.
The second threshold is set to reduce a calculation error generated by the existence of jumping points in the smoothing process, the second threshold may be 6cm, the corrected m point clouds in the region to be processed may be discarded when the m point clouds have jumping points, that is, the point clouds larger than the second threshold are smoothed, and errors of other point clouds may be caused if the jumping points are introduced into the calculation, so that the jumping points are removed from the m point clouds, and the remaining point clouds are averaged.
In some embodiments, after the point cloud in the corrected to-be-processed region is subjected to distance mean value smoothing processing once, distance mean value smoothing processing of the same operation may be further performed for multiple times, and the iteration number of the distance mean value smoothing processing is k.
By adopting the method, the distance average value smoothing operation is carried out on the point cloud notch output by the laser radar containing the casing cover or the protective cover, the point cloud transition of the notch part formed by the support column shielding is realized by using the method, the measurement accuracy is improved, the subsequent map building is greatly beautified, and a scheme for optimizing the output point cloud is provided for the laser radar product.
Fig. 12 is a schematic structural diagram of a lidar point cloud data distortion removal apparatus according to an embodiment of the present disclosure, and referring to fig. 12, the lidar point cloud data distortion removal apparatus 100 includes an obtaining module 101 and a processing module 102.
The acquisition module 101 is configured to acquire a calibration point cloud of a laser radar, and determine a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, where the support column is arranged around the laser radar;
the processing module 102 is configured to acquire point cloud data generated during ranging of the laser radar, determine a region to be processed in the point cloud data based on the first angle value, and perform distance correction and smoothing on the region to be processed to obtain target point cloud data.
It should be noted that the laser radar point cloud data distortion removal device can execute the laser radar point cloud data distortion removal method provided by the embodiment of the application, and has the beneficial effects of corresponding functional modules of the execution method. Technical details which are not described in detail in the embodiment of the laser radar point cloud data distortion removing device can be referred to the laser radar point cloud data distortion removing method provided by the embodiment of the application.
An embodiment of the present invention further provides a laser radar, please refer to fig. 13, and fig. 13 is a schematic structural diagram of the laser radar provided in the embodiment of the present application. The laser radar 200 includes: at least one processor 201, and a memory 202 communicatively coupled to the at least one processor 201; wherein the memory 202 stores instructions executable by the at least one processor 201 to enable the at least one processor 201 to perform a method of laser radar point cloud data de-distortion. The processor 201 and the memory 202 may be connected by a bus or other means, and fig. 13 illustrates the connection by a bus as an example.
The memory 202 is a readable storage medium, and can be used to store software programs, executable programs, and modules, such as program instructions/modules corresponding to the method for laser radar point cloud data distortion removal in the embodiments of the present application. The processor 201 executes various functional applications of the server and data processing by running software programs, instructions and modules stored in the memory 202, namely, the method for performing laser radar point cloud data distortion removal in the above embodiments is realized.
The memory 202 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the laser radar point cloud data undistorting device, and the like. The memory 202 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 202 may optionally include memory located remotely from the processor 201, which may be connected to the shelf over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 202, and when executed by the one or more processors 201, perform the method for laser radar point cloud data de-distortion in any of the above-described method embodiments, e.g., performing the method steps of fig. 5 described above, and implementing the functions of the modules in fig. 12.
The embodiment of the invention also provides a robot, wherein the robot comprises the laser radar in the figure 13, and the laser radar point cloud data distortion removing method in the embodiment can be realized. For example, the method steps of fig. 5 described above are performed to implement the functionality of the modules in fig. 12.
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, and the computer program can be stored in a 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 (10)
1. A method for laser radar point cloud data distortion removal, the method comprising:
obtaining calibration point cloud of a laser radar, and determining a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, wherein the support column is arranged at the periphery of the laser radar;
extending the first angle value by a first preset angle to obtain an angle area to be processed;
determining one or more reference angle values from the angle area to be processed, specifically including: judging whether point clouds are generated at the supporting columns or not; if the point cloud is generated at the supporting column, determining the angle value of the point cloud closest to the laser radar in the angle area to be processed as a reference angle value; if no point cloud is generated at the supporting column, calculating the absolute value of the distance difference between every two adjacent points in the angle area to be processed, and determining one or more reference angle values according to the absolute value of the distance difference;
acquiring point cloud data generated during laser radar ranging, determining a region to be processed in the point cloud data based on the first angle value, and determining one or more reference points from the region to be processed, wherein points corresponding to the reference angle values are the reference points; determining one or more sub-areas in the area to be processed according to the one or more reference points; performing distance correction on the point cloud of each sub-area in the area to be processed to obtain a corrected area to be processed; and smoothing the point cloud of the corrected region to be processed to obtain target point cloud data.
2. The method of claim 1, wherein determining one or more sub-regions of the region to be processed based on the one or more reference points comprises:
selecting a reference point, expanding preset number of point clouds on the left and right of the selected reference point respectively, or expanding a second preset angle on the left and right of the selected reference point respectively to obtain sub-areas corresponding to the selected reference point, wherein the point clouds in the area to be processed are ordered according to the generation time.
3. The method according to any one of claims 1 or 2, wherein the distance correction of the point cloud of each sub-area in the area to be processed comprises:
selecting a sub-area from the area to be processed, and taking the distance value of a first point in the sub-area as the distance correction value of each point on the left side of the reference point in the selected sub-area;
taking the distance value of the last point in the sub-area as the distance correction value of each point to the right of the reference point in the selected sub-area;
and taking the distance value of the first point or the distance value of the last point in the sub-area as the distance correction value of the reference point in the sub-area.
4. The method according to claim 1, wherein the smoothing of the point cloud of the rectified region to be processed comprises:
sequentially taking each point in the corrected region to be processed as an initial point, performing mean value smoothing on continuous m points, and outputting a smoothing result;
the mean smoothing processing on the continuous m points comprises the following steps:
and taking the average value of the continuous m points as the distance value of the m points after the starting point is smoothed.
5. The method of claim 4, further comprising:
detecting whether a jumping point exists in the m point clouds;
and if yes, carrying out mean value smoothing treatment on the point clouds except the jumping points in the m point clouds.
6. The method of claim 5, wherein the detecting whether the jumping points exist in the m point clouds comprises:
calculating a first difference absolute value of the distance between every two point clouds in the m point clouds;
recording the minimum value of the absolute value of the first difference as t;
calculating the average value of the distance values of the two point clouds corresponding to the t and recording the average value as r;
when the t is smaller than a first threshold value, calculating a second difference absolute value between the distance value of other point clouds except two point clouds corresponding to the t in the m point clouds and the r;
judging whether the absolute value of the second difference value is larger than a second threshold value;
and if so, determining the point cloud corresponding to the second difference absolute value as the jumping point.
7. The method of claim 6, further comprising:
and when the t is greater than or equal to a first threshold value, smoothing is not carried out on the m point clouds.
8. A laser radar point cloud data distortion removal device is characterized by comprising:
the acquisition module is used for acquiring calibration point cloud of the laser radar, and determining a first angle value of a support column relative to the laser radar according to position information of the support column and the calibration point cloud, wherein the support column is arranged around the laser radar;
the processing module is used for acquiring point cloud data generated in the laser radar ranging process, determining a region to be processed in the point cloud data based on the first angle value, and performing distance correction and smoothing on the region to be processed to obtain target point cloud data;
the laser radar point cloud data distortion removal device is used for executing the method of any one of claims 1 to 7.
9. A lidar, 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.
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