CN112362059B - Positioning method and device for mobile carrier, computer equipment and medium - Google Patents
Positioning method and device for mobile carrier, computer equipment and medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/12—Systems for determining distance or velocity not using reflection or reradiation using electromagnetic waves other than radio waves
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
- G01S15/06—Systems determining the position data of a target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
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Abstract
The present disclosure provides a positioning method of a mobile carrier, including: respectively acquiring map point cloud data and measurement point cloud data of the mobile carrier about the current environment; converting the map point cloud data into a plurality of grid maps with different resolutions; sequentially matching the measurement point cloud data with the grid maps according to the sequence of the resolution ratio from low to high until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio in the grid maps is obtained; and determining location information of the mobile carrier based on the location information of the optimal set of mapping points relative to the highest resolution grid map. The present disclosure also provides a positioning device for a mobile carrier, a computer device and a computer readable storage medium.
Description
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to a method, an apparatus, a computer device, and a medium for positioning a mobile carrier.
Background
With the continuous development of computer technology and internet technology, intelligent mobile carriers are widely used, for example, intelligent service robots have emerged in recent two years, and cleaning robots, home accompanying robots, meal delivery robots and the like have successively come into public view.
When discussing whether a mobile carrier can solve the practical problems in life and work of people, the autonomous positioning navigation technology is attracting attention as an intelligent first step.
In the prior art, a point cloud matching technology is generally utilized to realize the positioning of a mobile carrier, and specifically, a local matching method and a global matching method are included. The local matching method has high calculation efficiency, but is easy to sink into a local optimal solution, and the global matching method can prevent from sinking into the local optimal solution, but has large search range, low calculation efficiency and great time consumption.
Disclosure of Invention
In view of this, the present disclosure provides an improved method, apparatus, computer device and medium for positioning a mobile carrier.
One aspect of the present disclosure provides a positioning method of a mobile carrier, including: respectively acquiring map point cloud data and measurement point cloud data of the mobile carrier about the current environment; converting the map point cloud data into a plurality of grid maps with different resolutions; sequentially matching the measurement point cloud data with the grid maps according to the sequence of the resolution ratio from low to high until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio in the grid maps is obtained; and determining location information of the mobile carrier based on the location information of the optimal set of mapping points relative to a highest resolution grid map of the plurality of grid maps.
According to an embodiment of the present disclosure, the matching the measurement point cloud data with the plurality of grid maps sequentially includes: for any grid map in the plurality of grid maps, if a previous optimal mapping point set exists, matching the measurement point cloud data with the any grid map for an area corresponding to the previous optimal mapping point set of the any grid map to obtain an optimal mapping point set of the measurement point cloud data relative to the any grid map; and if the previous optimal point set does not exist, matching the measurement point cloud data with any grid map aiming at all areas of any grid map to obtain the optimal mapping point set of the measurement point cloud data relative to any grid map. The previous optimal mapping point set is the optimal mapping point set of the measurement point cloud data relative to the previous grid map of any grid map.
According to an embodiment of the present disclosure, the converting the map point cloud data into a plurality of grid maps having different resolutions includes: selecting any resolution, and determining the grid unit scale based on the any resolution; determining one or more point data located within the same grid cell scale based on the position information of each point data in the map point cloud data; performing Gaussian blur processing based on the depth values of the one or more point data to obtain pixel values corresponding to the same grid unit scale; and constructing a grid map of the arbitrary resolution based on the grid cell scale and the pixel values.
According to an embodiment of the present disclosure, the matching the measurement point cloud data with the arbitrary grid map includes: determining a basic step size and a basic rotation angle corresponding to the resolution based on the resolution of the one grid map; determining a plurality of transformation relationships based on the basic step size and the basic rotation angle; for any one of the plurality of transformation relationships, mapping the measurement point cloud data into the grid map according to the any one transformation relationship to obtain a mapping point set corresponding to the any one transformation relationship, and obtaining a score for the any one transformation relationship based on the mapping point set; determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships; and taking the mapping point set corresponding to the optimal transformation relation as the optimal mapping point set of the measurement point cloud data relative to the grid map.
According to an embodiment of the present disclosure, the obtaining the score for the arbitrary transformation relationship based on the mapping point set includes: acquiring pixel values of all mapping points in the mapping point set; and summing the acquired pixel values to obtain a score for any transformation relation.
According to an embodiment of the present disclosure, determining the optimal transformation relationship based on the scores for each of the plurality of transformation relationships includes: and taking the transformation relation with the highest score as the optimal transformation relation.
According to an embodiment of the present disclosure, the obtaining the score for the arbitrary transformation relationship based on the mapping point set includes: for any mapping point in the mapping point set, acquiring a deviation value between a pixel value of the any mapping point and a depth value of point data corresponding to the any mapping point in the measurement point cloud data; and summing the obtained deviation values to obtain a score for any transformation relation.
According to an embodiment of the present disclosure, determining the optimal transformation relationship based on the scores for each of the plurality of transformation relationships includes: and taking the transformation relation with the lowest score as the optimal transformation relation.
According to an embodiment of the present disclosure, the above method further includes: and before the measuring point cloud data are sequentially matched with the grid maps, if the measuring point cloud data comprise a plurality of point data with position coordinates in the same first preset range and depth values in the same second preset range, performing downsampling processing on the plurality of point data.
Another aspect of the present disclosure provides a positioning device for a moving carrier, including: the system comprises an acquisition module, a map conversion module, a matching module and a positioning module. The acquisition module is used for respectively acquiring map point cloud data and measurement point cloud data of the mobile carrier about the current environment. The map conversion module is used for converting the map point cloud data into a plurality of grid maps with different resolutions. And the matching module is used for sequentially matching the measurement point cloud data with the grid maps according to the sequence of the resolution ratio from low to high until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio in the grid maps is obtained. The positioning module is used for determining the position information of the mobile carrier based on the position information of the optimal mapping point set relative to the grid map with the highest resolution in the plurality of grid maps.
According to an embodiment of the present disclosure, the matching module is specifically configured to: and for any grid map in the plurality of grid maps, matching the measurement point cloud data with the any grid map aiming at the area of the any grid map corresponding to the previous optimal mapping point set if the previous optimal mapping point set exists, and matching the measurement point cloud data with the any grid map aiming at the whole area of the any grid map if the previous optimal mapping point set does not exist, so as to obtain the optimal mapping point set of the measurement point cloud data relative to the any grid map. The previous optimal mapping point set is the optimal mapping point set of the measurement point cloud data relative to the previous grid map of any grid map.
Another aspect of the present disclosure provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the program.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions which when executed are for implementing a method as described above.
According to the embodiment of the disclosure, the map point cloud data are converted into a plurality of grid maps with different multi-resolutions, the measured point cloud data and the multi-resolution grid maps are sequentially matched, the matching range is gradually reduced, the matching precision is improved, therefore, the local optimal solution is not involved, the optimal mapping point set of the measured point cloud data relative to the grid map with the highest resolution can be quickly and accurately found, and further accurate positioning is realized.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:
FIG. 1 schematically illustrates an exemplary system architecture for a positioning method and apparatus employing a mobile carrier in accordance with an embodiment of the present disclosure;
fig. 2A schematically illustrates a flow chart of a method of positioning a mobile carrier according to an embodiment of the disclosure;
FIG. 2B schematically illustrates a flow chart of a matching process of measured point cloud data according to an embodiment of the disclosure;
FIG. 3A schematically illustrates a schematic view of a grid map according to an embodiment of the present disclosure;
FIG. 3B schematically illustrates a schematic view of a plurality of grid maps in accordance with an embodiment of the present disclosure;
fig. 4 schematically illustrates a block diagram of a positioning device of a mobile carrier in accordance with an embodiment of the present disclosure;
fig. 5 schematically illustrates a block diagram of a positioning device of a mobile carrier according to another embodiment of the present disclosure; and
fig. 6 schematically illustrates a block diagram of a computer device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a positioning method and device for a mobile carrier. The method comprises a Point Cloud (Point Cloud) data acquisition stage, a map conversion stage, a matching stage and a positioning stage. And in the point cloud data acquisition stage, map point cloud data and measurement point cloud data of the mobile carrier about the current environment are respectively acquired. In the Map conversion stage, map point cloud data is converted into a plurality of Grid maps (Grid maps) having different resolutions (resolutions). And then entering a matching stage, and sequentially matching the measurement point cloud data with a plurality of grid maps according to the sequence of the resolution ratio from low to high until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio is obtained, which can be called as a final optimal mapping point set. Then, in the positioning stage, the position information of the mobile carrier is determined based on the position information of the final optimal mapping point set.
Fig. 1 schematically illustrates an exemplary system architecture 100 to which positioning methods and apparatus of a mobile carrier may be applied, according to embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.
As shown in fig. 1, a system architecture 100 according to this embodiment may include mobile carriers 101, 102, which mobile carriers 101, 102 may move within a geographic area 103. In a specific example, the mobile carriers 101 and 102 may be unmanned vehicles, intelligent robots, etc., the geographic range 103 may be a city, an indoor, a field environment, etc., and the positioning method and apparatus of the mobile carrier according to the embodiments of the present disclosure may be applied to, for example, a scenario where the intelligent robots perform delivery service in a city, or where the intelligent robots perform cleaning service in a room, or where the intelligent robots perform shooting activity in a field environment, etc., without limitation.
Point data for a plurality of points (as shown in fig. 1) in the geographic area 103 may be collected by a sensor such as a lidar to construct map point cloud data. When the mobile carriers 101 and 102 move to any position, point data of a plurality of points in the current environment can be collected through sensors such as self-configured laser radars and the like to form measurement point cloud data. The current geographical position of the mobile carrier 101, 102 may be obtained by matching the measurement point cloud data and the map point cloud data.
It should be understood that the type, number, type of geographical range, size, etc. of the mobile carriers in fig. 1 are merely illustrative and may be set according to actual needs.
Fig. 2A schematically illustrates a flow chart of a method of positioning a mobile carrier according to an embodiment of the disclosure.
As shown in fig. 2A, the method includes operations S210 to S240:
in operation S210, map point cloud data and measured point cloud data of the mobile carrier with respect to the current environment are acquired, respectively.
The map point cloud data is point cloud data about a predetermined geographical range, which is acquired in advance through large-scale point cloud data acquisition and can be used as reference data, and is essentially a set of a plurality of reference point data. The measured point cloud data of the mobile carrier about the current environment refers to point cloud data of the mobile carrier about the current environment, which is acquired by sensing devices such as a laser radar, a depth camera, an ultrasonic sensor and the like, and is essentially a set of a plurality of measured point data.
In operation S220, the map point cloud data is converted into a plurality of grid maps having different resolutions.
In operation S230, the measurement point cloud data is sequentially matched with the plurality of grid maps according to the order of the resolution from low to high until an optimal mapping point set of the measurement point cloud data with respect to the grid map with the highest resolution of the plurality of grid maps is obtained.
In this operation S230, the matching the measurement point cloud data with the plurality of grid maps sequentially includes: and for any grid map in the plurality of grid maps, if the previous optimal mapping point set exists, matching the measurement point cloud data with the any grid map for the area corresponding to the previous optimal mapping point set of the any grid map, so as to obtain the optimal mapping point set of the measurement point cloud data relative to the any grid map. And if the previous optimal point set does not exist, matching the measurement point cloud data with any grid map aiming at all areas of any grid map to obtain the optimal mapping point set of the measurement point cloud data relative to any grid map. The previous optimal mapping point set of any grid map is an optimal mapping point set of measurement point cloud data relative to another grid map, the other grid map is a grid map matched with the measurement point cloud data before any grid map, and the resolution of any grid map is higher than that of the other grid map.
In operation S240, location information of the mobile carrier is determined based on location information of an optimal mapping point set with respect to the highest resolution grid map.
Therefore, the method shown in fig. 2A converts the map point cloud data into a plurality of grid maps with different resolutions, matches the measured point cloud data with the grid maps with different resolutions in sequence, gradually reduces the matching range and improves the matching precision, does not fall into a local optimal solution, and can quickly and accurately find an optimal mapping point set of the measured point cloud data relative to the grid map with the highest resolution, thereby realizing positioning.
Fig. 2B schematically illustrates a flow chart of a matching process of measured point cloud data according to an embodiment of the present disclosure.
As shown in fig. 2B, a specific procedure of operations S230 to S240 in fig. 2A is shown, including operations S231 to S237:
in operation S231, a plurality of grid maps are organized into a grid map sequence in order of resolution from low to high.
In operation S232, for any one of the grid maps in the grid map sequence, it is determined whether there is a preceding grid map of the any one of the grid maps in the grid map sequence, if so, operation S233 is performed, and if not, operation S234 is performed.
If the previous grid map of any grid map exists in the grid map sequence, the fact that other grid maps in the grid map sequence are matched with the measurement point cloud data before the measurement point cloud data are matched with the any grid map is indicated, the grid map matched with the measurement point cloud data before any grid map is assumed to be the grid map A, and the optimal mapping point set of the measurement point cloud data relative to the grid map A is assumed to be X1. If the previous grid map of the any grid map does not exist in the grid map sequence, the any grid map is the lowest-resolution grid map in the plurality of grid maps, and the measured point cloud data is matched with the any grid map first.
In operation S233, the measurement point cloud data is matched with the arbitrary grid map for the region corresponding to the optimal mapping point set X1 in the arbitrary grid map.
In operation S234, the measurement point cloud data is matched with the arbitrary grid map for the entire area of the arbitrary grid map.
In operation S235, an optimal mapping point set X2 of the measured point cloud data with respect to the arbitrary grid map is obtained.
In operation S236, it is determined whether or not there is a subsequent raster map of the arbitrary raster map in the raster map sequence, and if yes, operation S232 is repeatedly executed with the subsequent raster map as the arbitrary raster map, and if no, operation S237 is executed.
If there is a subsequent raster map of the arbitrary raster map in the raster map sequence, which indicates that the arbitrary raster map is not the highest resolution raster map, the measured point cloud data needs to be continuously matched with the subsequent raster map according to operation S232. If the following grid map of any grid map does not exist in the grid map sequence, the fact that any grid map is the grid map with the highest resolution in the grid maps is indicated, and the optimal mapping point set X2 is the final optimal mapping point set of the measured point cloud data relative to the grid map with the highest resolution.
In operation S237, current location information of the mobile carrier is determined based on the location information of the optimal mapping point set X2.
As can be seen from the flowcharts shown in fig. 2A to 2B, in the process of matching the measurement point cloud data with the multi-resolution grid map, the measurement point cloud data is matched with each grid map in the area defined by the matching result with the previous grid map, and as the resolution of the matching grid map is gradually increased, the matching area is gradually decreased, on the one hand, the accuracy of the matching result can be improved, on the other hand, the searching efficiency of the matching result can be improved, and finally, the optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution is obtained, and the optimal mapping point set can reflect the positioning result with the highest resolution.
In one embodiment of the present disclosure, the converting map point cloud data into a plurality of grid maps having different resolutions includes: selecting any resolution, and determining the grid unit scale based on the any resolution; determining one or more point data located within the same grid cell scale based on the position information of each point data in the map point cloud data; performing Gaussian Blur (Gaussian blue) processing based on the depth values of the one or more point data to obtain pixel values corresponding to the same grid cell scale; and constructing a grid map of the arbitrary resolution based on the grid cell scale and the pixel values.
The resolution of the Grid map is a ground resolution (Ground Resolution) or a spatial resolution (Spatial Resolution), and the Grid Cell scale (Grid Cell Length) represents a ground actual distance or a spatial actual distance corresponding to each Grid Cell (Grid Cell), and the resolution of the Grid map may be equal to the inverse of the Grid Cell scale. In this example, it is assumed that 1000 point data are included in the map point cloud data,the ith (i is a positive integer less than or equal to 1000) point data a in the map point cloud data i Can be expressed as (x) i ，y i ，z i ) Wherein x is i And y i Representing the position information of the corresponding point, z j Depth information representing the corresponding point. Specifically, x i An abscissa, y, representing the projection of the corresponding point on the ground i Representing the ordinate, z, of the projection of the corresponding point on the ground i Representing the height of the corresponding point relative to the ground. In other examples, the point data in the map point cloud data may also be represented by polar coordinates or other coordinate forms, without limitation.
Fig. 3A schematically illustrates a schematic diagram of a grid map according to an embodiment of the present disclosure.
As shown in fig. 3A, assuming that the grid cell scale of the grid map is 0.1m, the resolution of the grid map is 1 m/0.1m=10. For each grid unit, knowing which points fall into the grid unit according to the position information of each point data in the map point cloud data, and carrying out Gaussian blur processing on the depth values of the points to obtain a pixel value of the grid unit, and representing the pixel value by the corresponding gray scale to obtain the grid map shown in (a) in fig. 3A. Grid maps of either resolution may be obtained as described above.
Further, a low resolution grid map may also be generated based on the high resolution grid map. Continuing back to fig. 3A, for example, the grid cell scale of the grid map of (a) is 0.1m, four of the grid cells are combined into one new grid cell, and the pixel value of each new grid cell can be obtained by processing the pixel values of the original four grid cells, for example, the maximum pixel value corresponding to the original four grid cells can be used as the pixel value of the new grid cell, so that the grid map of the grid cell scale of 0.2m shown in (b) can be obtained. Similarly, the combination processing can be performed on different numbers of grid units according to the requirement, so as to obtain grid maps with different resolutions.
Fig. 3B schematically illustrates a schematic diagram of a plurality of grid maps according to an embodiment of the present disclosure.
As shown in fig. 3B, as an example, map point cloud data is converted into three grid maps with different resolutions: a grid map a with a grid cell scale of 0.1m and a resolution of 10 shown in (a) of fig. 3B, a grid map B with a grid cell scale of 0.5m and a resolution of 2 shown in (B) of fig. 3B, and a grid map C with a grid cell scale of 1m and a resolution of 1 shown in (C) of fig. 3B. In this example, the resolution of the grid map a > the resolution of the grid map B > the resolution of the grid map C.
After obtaining the multi-resolution grid map, the measurement point cloud data is sequentially matched with the plurality of grid maps in order of resolution from low to high, and in the example shown in fig. 3B, for example, the measurement point cloud data is first matched with the grid map C to obtain an optimal mapping point set C' with respect to the grid map C, the optimal mapping point set corresponding to the region 1 in the grid map B. And matching the measured point cloud data with the region 1 in the grid map B to obtain an optimal mapping point set B' relative to the grid map B, wherein the optimal mapping point set corresponds to the region 2 in the grid map A. And matching the measurement point cloud data with the region 2 in the grid map A to obtain an optimal mapping point set A 'relative to the grid map A, and determining the position coordinate of the optimal mapping point set A' in the grid map A as the current position coordinate of the mobile carrier.
In one embodiment of the present disclosure, matching the measurement point cloud data with one of a plurality of grid maps may include: determining a basic step size and a basic rotation angle corresponding to the resolution of the grid map based on the resolution of the grid map; determining a plurality of transformation relationships based on the basic step size and the basic rotation angle; for any one of the plurality of transformation relationships, mapping the measurement point cloud data into the grid map according to the any one transformation relationship to obtain a mapping point set corresponding to the any one transformation relationship, and obtaining a score for the any one transformation relationship based on the mapping point set; determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships; and taking the mapping point set corresponding to the optimal transformation relation as the optimal mapping point set of the measurement point cloud data relative to the grid map.
The basic step size determined based on the resolution of the grid map may be smaller than or equal to the grid cell dimension (expressed as a length) of the grid map in a rectangular coordinate system, and the basic rotation angle determined based on the resolution of the grid map may be smaller than or equal to the grid cell dimension (expressed as an angle) of the grid map in a polar coordinate system, so that each grid cell of the grid map can be traversed through a combination of the basic step size and/or the basic rotation angle. Each transformation relationship may include one or more translations, each of which may be an integer multiple of the base step size, and each transformation relationship may include one or more rotations, each of which may be an integer multiple of the base rotation angle.
The above-described process of obtaining the optimal mapping point set is described below by way of two embodiments.
Embodiment one:
the obtaining the score for the arbitrary transformation relationship based on the mapping point set may include: acquiring pixel values of all mapping points in the mapping point set; and summing the acquired pixel values to obtain a score for any transformation relation.
Accordingly, determining the optimal transformation relationship based on the scores for each of the plurality of transformation relationships includes: and taking the transformation relation with the highest score as the optimal transformation relation.
By way of example, the following is illustrative: let the measured point cloud data include j (j is a positive integer) point data, j-th point data b j Can be expressed as (x) j ，y j ，z j ) Wherein x is j And y j Representing the position information of the corresponding point, z j Depth information representing the corresponding point. Specifically, x j An abscissa, y, representing the projection of the corresponding point on the ground j Representing the ordinate, z, of the projection of the corresponding point on the ground j Representing the height of the corresponding point relative to the ground. In other examples, the measurement pointsThe point data in the cloud data may also be represented in polar coordinates or other coordinate forms, without limitation. When matching the measurement point cloud data with the grid map B, a transformation relation R capable of traversing the grid map B is obtained based on the combination of basic step length and basic rotation angle m For example, m (m is a positive integer): transformation relation R 1 Transform relationship R 2 … … and transformation relation R m . Then according to the transformation relation R 1 Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } 1j ＝(x 1j ，y 1j ，z 1j ) According to the transformation relation R 2 Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } 2j ＝(x 2j ，y 2j ，z 2j ) And … … according to the transformation relation R m Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } mj ＝(x mj ，y mj ，z mj )}。
By way of example, transform relationship R m Score S of (2) m Can be calculated by formula (1) (assuming the maximum value of j is N):
on this basis, the optimal transformation relationship R can be obtained by the formula (2):
R * ＝argmaxS m (R m )
(2)
in the embodiment, the transformation relation with the highest score is used as the optimal transformation relation, and the mapping point set corresponding to the optimal transformation relation can be used as the optimal mapping point set of the test point cloud data relative to the grid map B.
Embodiment two:
the obtaining the score for the arbitrary transformation relationship based on the mapping point set may include: for any mapping point in the mapping point set, acquiring a deviation value between a pixel value of the any mapping point and a depth value of point data corresponding to the any mapping point in the measurement point cloud data; and summing the obtained deviation values to obtain a score for any transformation relation.
Accordingly, determining the optimal transformation relationship based on the scores for each of the plurality of transformation relationships includes: and taking the transformation relation with the lowest score as the optimal transformation relation.
Taking the example above, assuming that the measurement point cloud data includes j (j is a positive integer) point data, the measurement point cloud data may be expressed as { b } j ＝(x j ，y j ，z j ) }. When matching the measurement point cloud data with the grid map B, a transformation relation R capable of traversing the grid map B is obtained based on the combination of basic step length and basic rotation angle m For example, m (m is a positive integer): transformation relation R 1 Transform relationship R 2 … … and transformation relation R m . Then according to the transformation relation R 1 Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } 1j ＝(x 1j ，y 1j ，z 1j ) According to the transformation relation R 2 Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } 2j ＝(x 2j ，y 2j ，z 2j ) And … … according to the transformation relation R m Mapping the measurement point cloud data to the grid map B to obtain a mapping point set { c } mj ＝(x mj ，y mj ，z mj )}。
By way of example, transform relationship R m Score S of (2) m Can be calculated by formula (3) (assuming the maximum value of j is N):
on this basis, the optimal transformation relationship R can be obtained by the formula (4):
R * ＝argminS m (R m )
(4)
in the embodiment, the transformation relation with the lowest score is used as the optimal transformation relation, and the mapping point set corresponding to the optimal transformation relation can be used as the optimal mapping point set of the test point cloud data relative to the grid map B.
In one embodiment of the present disclosure, the positioning method of the mobile carrier according to the embodiment of the present disclosure may further include: before sequentially matching the measurement point cloud data with the plurality of grid maps, if the measurement point cloud data includes a plurality of point data whose position coordinates are within the same first predetermined range and whose depth values are within the same second predetermined range, downsampling (downsampling) is performed on the plurality of point data. According to the embodiment, if there are a plurality of point data with adjacent positions and similar depth values in the measured point cloud data, the plurality of point data are point data with similar characteristics, for example, the plurality of point data represent a plurality of points on the same obstacle (for example, a plurality of points on the roof of the same building), and at this time, the plurality of points can be processed into fewer points through the downsampling process, so that the overall number of point data in the measured point cloud data is reduced, and the calculation amount of the point cloud matching process is further reduced to a certain extent.
Fig. 4 schematically illustrates a block diagram of a positioning device for moving a carrier according to an embodiment of the present disclosure.
As shown in fig. 4, the positioning device 400 for moving the carrier includes: an acquisition module 401, a map conversion module 402, a matching module 403, and a positioning module 404.
The acquiring module 401 is configured to acquire map point cloud data and measurement point cloud data of the mobile carrier about a current environment respectively.
The map conversion module 402 is configured to convert the map point cloud data into a plurality of grid maps having different resolutions.
The matching module 403 is configured to match the measurement point cloud data with the plurality of grid maps sequentially according to the order of the resolution from low to high until an optimal mapping point set of the measurement point cloud data with respect to a grid map with a highest resolution of the plurality of grid maps is obtained.
The positioning module 404 is configured to determine the location information of the mobile carrier based on the location information of the optimal set of mapping points relative to the highest resolution grid map of the plurality of grid maps.
In one embodiment of the present disclosure, the matching module 403 is specifically configured to: and for any grid map in the plurality of grid maps, matching the measurement point cloud data with the any grid map aiming at the area of the any grid map corresponding to the previous optimal mapping point set if the previous optimal mapping point set exists, and matching the measurement point cloud data with the any grid map aiming at the whole area of the any grid map if the previous optimal mapping point set does not exist, so as to obtain the optimal mapping point set of the measurement point cloud data relative to the any grid map. The previous optimal mapping point set is an optimal mapping point set of the measurement point cloud data relative to a previous grid map of any grid map.
Fig. 5 schematically illustrates a block diagram of a positioning device for moving a carrier according to another embodiment of the present disclosure.
As shown in fig. 5, the positioning device 500 for moving the carrier includes: an acquisition module 501, a map conversion module 502, a matching module 503, and a positioning module 504. The obtaining module 501, the map converting module 502, the matching module 503, and the positioning module 504 have the same functions as those of the obtaining module 401, the map converting module 402, the matching module 403, and the positioning module 404, and repeated parts are not described again.
In one embodiment of the present disclosure, the map conversion module 502 includes: the submodule 5021, the first determining submodule 5022, the processing submodule 5023 and the building submodule 5024 are selected.
The selecting submodule 5021 is used for selecting any resolution and determining the grid cell scale based on the any resolution. The first determining submodule 5022 is used for determining one or more point data located in the same grid cell scale based on the position information of each point data in the map point cloud data. The processing submodule 5023 is used for performing gaussian blur processing based on the depth values of the one or more point data to obtain pixel values corresponding to the same grid unit scale. And a construction sub-module 5024 for constructing a grid map of the arbitrary resolution based on the grid cell scale and the pixel values.
In one embodiment of the present disclosure, the matching module 503 includes: a second determination submodule 5031, a third determination submodule 5032, a mapping scoring submodule 5033, and a fourth determination submodule 5034.
The second determining submodule 5031 is used for determining a basic step size and a basic rotation angle corresponding to the resolution based on the resolution of the one grid map. The third determining submodule 5032 is configured to determine a plurality of transformation relations based on the basic step size and the basic rotation angle. The mapping evaluation sub-module 5033 is configured to map, for any one of the plurality of transformation relationships, the measurement point cloud data into the one grid map according to the any one transformation relationship, obtain a mapping point set corresponding to the any one transformation relationship, and obtain a score for the any one transformation relationship based on the mapping point set. A fourth determination submodule 5034 for determining an optimal transformation relation based on the score for each transformation relation of the plurality of transformation relations; and taking the mapping point set corresponding to the optimal transformation relation as the optimal mapping point set of the measurement point cloud data relative to the grid map.
Specifically, as an optional embodiment, the mapping evaluation sub-module 5033 is specifically configured to obtain a pixel value of each mapping point in the mapping point set; and summing the acquired pixel values to obtain a score for any transformation relation. The fourth determination submodule 5034 is specifically configured to take the highest-scoring transformation relation as the optimal transformation relation.
Specifically, as another optional embodiment, the mapping evaluation sub-module 5033 is specifically configured to obtain, for any mapping point in the mapping point set, a deviation value between a pixel value of the any mapping point and a depth value of point data corresponding to the any mapping point in the measured point cloud data; and summing the obtained deviation values to obtain a score for any transformation relation. The fourth determination submodule 5034 is specifically configured to take the transformation relation with the lowest score as the optimal transformation relation.
In one embodiment of the present disclosure, the positioning device 500 of the mobile carrier may further include a downsampling module 505 for performing downsampling processing on the plurality of point data if the measured point cloud data includes a plurality of point data whose position coordinates are within the same first predetermined range and whose depth values are within the same second predetermined range before the matching module 503 matches the measured point cloud data with the plurality of grid maps in sequence.
It should be noted that, in the embodiment of the apparatus portion, the implementation manner, the solved technical problem, the realized function, and the achieved technical effect of each module/unit/subunit and the like are the same as or similar to the implementation manner, the solved technical problem, the realized function, and the achieved technical effect of each corresponding step in the embodiment of the method portion, and are not described herein again.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.
For example, any of the acquisition module 501, map conversion module 502, matching module 503, positioning module 504, and downsampling processing module 505 may be combined in one module to be implemented, or any of the modules may be split into multiple modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the acquisition module 501, the map conversion module 502, the matching module 503, the positioning module 504, and the downsampling processing module 505 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the acquisition module 501, the map conversion module 502, the matching module 503, the positioning module 504, and the downsampling processing module 505 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.
Fig. 6 schematically shows a block diagram of a computer device adapted to implement the above-described method according to an embodiment of the present disclosure. The computer device illustrated in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 6, a computer device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 603, various programs and data required for the operation of the apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the device 600 may further include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The device 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (11)
1. A method of positioning a moving carrier, comprising:
respectively acquiring map point cloud data and measurement point cloud data of the mobile carrier about the current environment;
converting the map point cloud data into a plurality of grid maps with different resolutions;
sequentially matching the measurement point cloud data with the plurality of grid maps according to the sequence of the resolution ratio from low to high, wherein the matching of the measurement point cloud data with each grid map is performed in an area defined by a matching result with a previous grid map until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio in the plurality of grid maps is obtained;
Determining location information of the mobile carrier based on the location information of the optimal set of mapping points relative to a highest resolution grid map of the plurality of grid maps;
wherein, for any one of the plurality of grid maps, if there is a previous optimal mapping point set, determining a basic step size and a basic rotation angle corresponding to a resolution of the one grid map based on the resolution for an area of the any one grid map corresponding to the previous optimal mapping point set;
determining a plurality of transformation relationships based on the basic step size and the basic rotation angle;
for any one of the plurality of transformation relationships, mapping the measurement point cloud data into the grid map according to the any one transformation relationship to obtain a mapping point set corresponding to the any one transformation relationship, and obtaining a score for the any one transformation relationship based on the mapping point set;
determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships; and
and taking the mapping point set corresponding to the optimal transformation relation as the optimal mapping point set of the measurement point cloud data relative to the grid map.
2. The method of claim 1, wherein the sequentially matching the measurement point cloud data with the plurality of grid maps comprises:
for any one of the plurality of grid maps, if the previous optimal point set does not exist, matching the measurement point cloud data with the any grid map for all areas of the any grid map to obtain the optimal mapping point set of the measurement point cloud data relative to the any grid map,
the previous optimal mapping point set is an optimal mapping point set of the measurement point cloud data relative to a previous grid map of the any grid map.
3. The method of claim 1, wherein the converting the map point cloud data into a plurality of grid maps having different resolutions comprises:
selecting any resolution, and determining the grid unit scale based on the any resolution;
determining one or more point data located within the same grid cell scale based on the position information of each point data in the map point cloud data;
performing Gaussian blur processing based on the depth values of the one or more point data to obtain pixel values corresponding to the same grid unit scale; and
And constructing the grid map with any resolution based on the grid unit scale and the pixel value.
4. The method of claim 1, wherein the obtaining a score for the any transformation relationship based on the set of mapped points comprises:
acquiring pixel values of all mapping points in the mapping point set; and
and summing the acquired pixel values to obtain a score for any transformation relation.
5. The method of claim 4, wherein the determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships comprises: and taking the transformation relation with the highest score as the optimal transformation relation.
6. The method of claim 1, wherein the obtaining a score for the any transformation relationship based on the set of mapped points comprises:
for any mapping point in the mapping point set, acquiring a deviation value between a pixel value of the any mapping point and a depth value of point data corresponding to the any mapping point in the measurement point cloud data; and
and summing the obtained deviation values to obtain a score for any transformation relation.
7. The method of claim 6, wherein the determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships comprises: and taking the transformation relation with the lowest score as the optimal transformation relation.
8. The method of claim 1, further comprising:
and before the measuring point cloud data are sequentially matched with the grid maps, if the measuring point cloud data comprise a plurality of point data with position coordinates in the same first preset range and depth values in the same second preset range, performing downsampling processing on the plurality of point data.
9. A positioning device for a moving carrier, comprising:
the acquisition module is used for respectively acquiring map point cloud data and measurement point cloud data of the mobile carrier about the current environment;
the map conversion module is used for converting the map point cloud data into a plurality of grid maps with different resolutions;
the matching module is used for sequentially matching the measurement point cloud data with the grid maps according to the sequence of the resolution ratio from low to high, and the matching of the measurement point cloud data with each grid map is performed in an area limited by a matching result of the measurement point cloud data with the previous grid map until an optimal mapping point set of the measurement point cloud data relative to the grid map with the highest resolution ratio in the grid maps is obtained; and
a positioning module for determining position information of the mobile carrier based on the position information of the optimal mapping point set relative to the highest resolution grid map of the plurality of grid maps;
Wherein, for any one of the plurality of grid maps, if there is a previous optimal mapping point set, determining a basic step size and a basic rotation angle corresponding to a resolution of the one grid map based on the resolution for an area of the any one grid map corresponding to the previous optimal mapping point set;
determining a plurality of transformation relationships based on the basic step size and the basic rotation angle;
for any one of the plurality of transformation relationships, mapping the measurement point cloud data into the grid map according to the any one transformation relationship to obtain a mapping point set corresponding to the any one transformation relationship, and obtaining a score for the any one transformation relationship based on the mapping point set;
determining an optimal transformation relationship based on the scores for each of the plurality of transformation relationships; and
and taking the mapping point set corresponding to the optimal transformation relation as the optimal mapping point set of the measurement point cloud data relative to the grid map.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing when executing the program:
A method of positioning a moving carrier according to any one of claims 1 to 8.
11. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform:
a method of positioning a moving carrier according to any one of claims 1 to 8.
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