CN110741282B - External parameter calibration method, device, computing equipment and computer storage medium - Google Patents
External parameter calibration method, device, computing equipment and computer storage medium Download PDFInfo
- Publication number
- CN110741282B CN110741282B CN201980002761.1A CN201980002761A CN110741282B CN 110741282 B CN110741282 B CN 110741282B CN 201980002761 A CN201980002761 A CN 201980002761A CN 110741282 B CN110741282 B CN 110741282B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- sequence
- laser radar
- map
- cloud sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000009466 transformation Effects 0.000 claims abstract description 116
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 105
- 230000004807 localization Effects 0.000 claims description 21
- 238000013507 mapping Methods 0.000 claims description 21
- 238000004891 communication Methods 0.000 claims description 17
- 230000001131 transforming effect Effects 0.000 claims description 17
- 238000005457 optimization Methods 0.000 claims description 16
- 238000009826 distribution Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000033001 locomotion Effects 0.000 description 7
- 238000004590 computer program Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The embodiment of the invention discloses an external parameter calibration method, an external parameter calibration device, a computing device and a computer storage medium, wherein the external parameter calibration method comprises the following steps: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of a first laser radar and a second point cloud map of a second laser radar at preset time according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, by utilizing the scheme of the invention, the multi-laser radar with insufficient overlapping area can perform external parameter calibration by using a general point cloud registration algorithm.
Description
Technical Field
The invention relates to the technical field of laser radars, in particular to an external parameter calibration method, an external parameter calibration device, computing equipment and a computer storage medium.
Background
Lidar (light detection and rangi ng) is an optical remote sensing technique that measures parameters such as the distance of a target by irradiating a pulsed laser to the target. The collection of three-dimensional measurement points obtained by lidar may be referred to as a point cloud (point cloud) because of the large and relatively dense data volume. The point cloud registration is to set two three-dimensional data point sets from different coordinate systems, find the transformation relation of the two point set spaces, so that the two point sets can be unified into the same coordinate system. The extrinsic calibration is used to determine rotational and translational relationships between multiple sensor coordinate systems in order to represent multiple sensor data in a unified coordinate system.
The point cloud registration algorithm is based on the premise that two point clouds have overlapping portions, such as a portion of an object commonly irradiated by two lidars. In unmanned applications, there are cases where there are few or no overlapping portions of point clouds between multiple radars. For example, one laser radar is arranged in front of a vehicle head, one radar is arranged at the vehicle tail, and the two radars have no overlapping area due to the shielding of the vehicle body. In which case the generic registration cannot be used directly.
Disclosure of Invention
In view of the foregoing, embodiments of the present invention provide an external parameter calibration method, apparatus, computing device, and computer storage medium that overcome or at least partially solve the foregoing problems.
According to one aspect of the present invention, there is provided an external parameter calibration method, including: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result.
Optionally, the first lidar and the second lidar, moving or rotating, are located in a venue having a structured feature.
Optionally, the obtaining the first point cloud map of the first lidar at the preset moment according to the first point cloud sequence includes: applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the first point cloud sequence; transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new first point cloud sequence; combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment; the obtaining the second point cloud map of the second laser radar at the preset moment according to the second point cloud sequence includes: applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence; transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence; and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map.
Optionally, the applying a simultaneous localization and mapping algorithm calculates a transformation relationship of adjacent point clouds in the first point cloud sequence, including: traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm; optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm; the application of the simultaneous localization and mapping algorithm calculates a transformation relationship of adjacent point clouds in the second point cloud sequence, including: traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm; and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
Optionally, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
Wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j-1 ,P j )。
Optionally, the calculating, by using a point cloud registration algorithm, a transformation relationship of the second point cloud map transformed to the first point cloud map, and before obtaining the calibration result, includes: and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
Optionally, the point cloud registration algorithm includes an iterative closest point algorithm or a normal distribution transformation algorithm.
According to another aspect of the present invention, there is provided an external parameter calibration device including: the system comprises a data acquisition unit, a first laser radar detection unit and a second laser radar detection unit, wherein the data acquisition unit is used for acquiring at least a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; the first registration unit is used for respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and the calibration unit is used for calculating the transformation relation from the second point cloud map to the first point cloud map by applying a point cloud registration algorithm to obtain a calibration result.
According to another aspect of the present invention, there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the steps of the external parameter calibration method.
According to another aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform the steps of the aforementioned external parameter calibration method.
In an embodiment of the present invention, the external parameter calibration method includes: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, external parameter calibration of the laser radars is realized, and multiple laser radars with insufficient overlapping areas can be subjected to external parameter calibration by using a general point cloud registration algorithm.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures of the drawings are not to be taken in a limiting sense, unless otherwise indicated.
FIG. 1 shows a flow diagram of an extrinsic calibration method according to an embodiment of the invention;
FIG. 2 is a flow chart of another external parameter calibration method according to an embodiment of the invention;
FIG. 3 shows a schematic view of a point cloud before calibration of an external parameter calibration method according to an embodiment of the invention;
FIG. 4 shows a point cloud schematic diagram after calibration of two lidars according to an embodiment of the present invention;
FIG. 5 shows a point cloud schematic diagram after calibration of three lidars according to an embodiment of the present invention;
FIG. 6 shows a schematic structural diagram of a further external reference calibration device according to an embodiment of the invention;
FIG. 7 illustrates a schematic diagram of a computing device, according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 shows a flow chart of an external parameter calibration method according to an embodiment of the invention. As shown in fig. 1, the external parameter calibration method includes:
step S11: at least a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar are acquired, wherein the first point cloud sequence is located in a body coordinate system of the first laser radar, and the second point cloud sequence is located in the body coordinate system of the second laser radar.
In step S11, the first and second moving or rotating lidars are located in a venue having a structured feature. In an embodiment of the invention, a venue is selected that includes rich structured features; moving or rotating a carrier carrying at least the first and second lidars over the fieldThe method comprises the steps of carrying out a first treatment on the surface of the And simultaneously acquiring the first point cloud sequence of the first laser radar and the second point cloud sequence of the second laser radar. Taking two laser radars as an example, selecting a site rich in wall surfaces and rod-shaped objects, enabling a carrier carrying the first laser radar and the second laser radar to linearly advance at a constant speed of 5km/h for 10 meters, then driving according to an 8 shape, and simultaneously acquiring point cloud sequences of the two laser radars. The first point cloud sequence is located in a body coordinate system of the first laser radar, preferably, the body coordinate system of the first laser radar may be a coordinate system taking the center of the first laser radar as an origin, and the first point cloud sequence is denoted as L 1 ＝{P 0 ，P 1 ,…P n And n is a positive integer. The second point cloud sequence is located in the body coordinate system of the second laser radar, preferably, the body coordinate system of the second laser radar may be a coordinate system with the center of the second laser radar as the origin, and the second point cloud sequence is represented as l2= { P0, P1, … P m M is a positive integer.
The selection of sites that include rich structured features such as walls, rods, etc. is easy to map subsequently. In the embodiment of the invention, a plurality of laser radars can be rigidly arranged on a carrier, the carrier runs at a low speed and a uniform speed, the motion track comprises a straight line or a curve, and the point cloud sequence of each laser radar is collected.
Step S12: and respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence.
The preset time may be an initial time of the first point cloud sequence and the second point cloud sequence, or any other time of the selected first point cloud sequence and the second point cloud sequence in the acquisition process, which is not limited herein.
In the following, the first point cloud sequence and the second point cloud sequence are respectively described, and in the embodiment of the present invention, as shown in fig. 2, step S12 includes:
Step S121: and applying a simultaneous localization and mapping algorithm to calculate the transformation relation of adjacent point clouds in the first point cloud sequence.
Specifically, traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm; and optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm. The transformation relationship of the adjacent point cloud can be a simple coordinate transformation relationship of the adjacent point cloud, or can be other transformation relationships, such as scaling and the like. For the first point cloud sequence L 1 From 1 to n, using a point cloud simultaneous localization and mapping (Simultaneous localization and mapping, SLAM) algorithm, for example, using a normal distribution transformation (Normal Distributions Transform, NDT) algorithm or an iterative closest point (Iterative Closest Point, ICP) algorithm, optimizing by using a general graph optimization algorithm (General Graph Optimization, G2O), and calculating to obtain a first point cloud sequence L 1 Coordinate transformation relation T of adjacent point clouds in the middle i ＝F(P i-1 ，P i ) Wherein P is i-1 P is the coordinate of the i-1 th point cloud in the first point cloud sequence i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i Is a transformation relation of (a).
Step S122: and transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the coordinate transformation relation of the adjacent point clouds to form a new first point cloud sequence.
In the embodiment of the present invention, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )。
As shown in FIG. 3, a new calibration is obtainedIs denoted as L' 1 ＝{P' 0 ，P' 1 ,…P' n A new second point cloud sequence denoted L' 2 ＝{P' 0 ，P' 1 ,…P' m }。
In the embodiment of the present invention, preferably, the starting time is selected, and the first point cloud sequence and the second point cloud sequence are transformed to the body coordinate system of the corresponding lidar at the starting time. The first point cloud sequence is transformed to the body coordinate system of the first laser radar at the starting moment, and the second point cloud sequence is transformed to the body coordinate system of the second laser radar at the starting moment.
Step S123: and merging the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment.
And for the second point cloud sequence, applying a simultaneous localization and mapping algorithm, and calculating the transformation relation of adjacent point clouds in the second point cloud sequence. Specifically, traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm; and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm. For the second point cloud sequence L 2 From 1 to m, calculating to obtain a first point cloud sequence L by using SLAM algorithm 2 Coordinate transformation relation T of adjacent point clouds in the middle j =f (Pj-1, pj), where P j-1 P is the coordinate of the j-1 th point cloud in the first point cloud sequence j T is the coordinate of the jth point cloud in the first point cloud sequence j For adjacent point clouds P in the first point cloud sequence j-1 And P j Is a transformation relation of (a).
Then transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence; and combining the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map. Wherein the new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
Wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j -1,P j ). The new second point cloud sequence before calibration is obtained and is expressed as L' 2 ＝{P' 0 ，P' 1 ,…P' m }. New first point cloud sequence L 'before calibration' 1 In a single-frame point cloud and a new second point cloud sequence L 'before calibration' 2 The single frame point cloud of (a) is shown in figure 3.
In the embodiment of the invention, the first point cloud map and the second point cloud map are respectively based on different machine body coordinate systems at the same preset time. Combining n point clouds in the new first point cloud sequence before calibration to obtain a first point cloud map M of a first laser radar organism coordinate system based on the preset moment selected before calibration 1 First point cloud map M 1 Corresponding to the superposition of n point clouds in the first point cloud sequence, the shape of which is L 'in FIG. 3' 1 Similarly, but with more densely packed dots. Correspondingly, merging M point clouds in the new second point cloud sequence to obtain a second point cloud map M of a body coordinate system of the second laser radar based on the previously selected preset moment before calibration 2 . Second point cloud map M 2 Corresponds to the superposition of m point clouds in the second point cloud sequence, the shape of which is L 'in figure 3' 2 Similarly, but with more densely packed dots. First point cloud map M 1 And a second point cloud map M 2 Partially overlapping.
Step S13: and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result.
Specifically, a point cloud registration algorithm is applied to calculate a second point cloud map M 2 Transforming to the first point cloud map M 1 Is switched off by (2)The transformation matrix T=F (M 1 ,M 2 ) The transformation matrix T is the final calibration matrix. The point cloud registration algorithm includes an iterative closest point algorithm (ICP) or a normal distribution transformation algorithm (NDT), and in other embodiments of the present invention, other point cloud registration algorithms may be applied, which is not limited herein. In the embodiment of the invention, the first point cloud map M can also be calculated 1 Transforming to a second point cloud map M 2 Is a transformation relation of (a). According to the calibration result, a second point cloud map M 2 And a first point cloud map M 1 The calibrated point cloud map is obtained by calibration, and the calibration process is equivalent to the process of calibrating the first point cloud map M 1 And a second point cloud map M 2 Transformed to the same coordinate system, thus passing through the first point cloud map M 1 And a second point cloud map M 2 The calibrated point cloud map obtained after calibration is equivalent to the first point cloud map M 1 Or a second point cloud map M 2 Offset a distance and/or rotated a certain angle relative to the other to overlap as much as possible.
To accelerate the calculation, filtering operations are performed on the obtained first point cloud map and the second point cloud map, respectively, before step S13. Specifically, filtering operations such as thinning, denoising, feature point extraction and the like are respectively performed on the first point cloud map and the second point cloud map so as to reduce data quantity, improve data quality and facilitate subsequent calibration. The thinning algorithm comprises a grid downsampling algorithm, such as an octree grid thinning algorithm.
In the embodiment of the invention, a second point cloud map M is obtained 2 Relative to a first point cloud map M 1 After the calibration structure of (a) i.e. the calibration matrix T, the calibration matrix T can be directly applied to the second point cloud sequence L of the second lidar 2 And a first point cloud sequence L of a first laser radar 1 Calibrating, fig. 4 shows a second point cloud sequence L of the second laser radar 2 And a first point cloud sequence L of a first laser radar 1 And (5) calibrating the point cloud.
When there are multiple lidars, the point cloud sequence of each lidar can be acquired respectively relative to one of the lidars And then calibrating the calibration matrixes of the point cloud sequences. Such as a point cloud sequence L for three lidars 1 、L 2 、L 3 Firstly, respectively acquiring a point cloud sequence L of a second laser radar 2 Point cloud sequence L relative to first laser radar 1 Is used for calibrating the matrix of the third laser radar and the point cloud sequence L of the third laser radar 3 Point cloud sequence L relative to first laser radar 1 And then calibrating the point cloud sequences of the three laser radars to obtain calibrated point clouds. Or firstly acquiring calibration matrixes of the point cloud sequences of any two laser radars, and then calibrating each calibration matrix. Such as a point cloud sequence L for three lidars 1 、L 2 、L 3 Firstly, respectively acquiring a point cloud sequence L of a second laser radar 2 Point cloud sequence L relative to first laser radar 1 Is used for calibrating the matrix of the third laser radar and the point cloud sequence L of the third laser radar 3 Point cloud sequence L relative to second laser radar 2 And then calibrating the point cloud sequences of the three laser radars according to the two calibration matrixes to obtain calibrated point clouds. The calibrated point clouds finally obtained by the two methods are the same. FIG. 5 is a point cloud sequence L for three lidars 1 、L 2 、L 3 And (5) calibrating the point cloud.
In an embodiment of the present invention, the external parameter calibration method includes: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, laser radar external parameter calibration is realized, and multiple laser radars with insufficient overlapping areas can be calibrated by using a point cloud registration algorithm.
FIG. 6 shows a schematic structural diagram of an external parameter calibration device according to an embodiment of the present invention. As shown in fig. 6, the external parameter calibration device includes: a data acquisition unit 601, a registration unit 602 and a calibration unit 603. Wherein:
the data acquisition unit 601 is configured to acquire at least a first point cloud sequence of a moving or rotating first lidar and a second point cloud sequence of a second lidar, where the first point cloud sequence is located in a body coordinate system of the first lidar, and the second point cloud sequence is located in a body coordinate system of the second lidar; the registration unit 602 is configured to obtain a first point cloud map of the first lidar and a second point cloud map of the second lidar at preset moments according to the first point cloud sequence and the second point cloud sequence, respectively; the calibration unit 603 is configured to calculate a transformation relationship from the second point cloud map to the first point cloud map by applying a point cloud registration algorithm, so as to obtain a calibration result.
In an alternative, the first lidar and the second lidar, which are either moving or rotating, are located in a venue having a structured feature.
In an alternative way, the registration unit 602 is configured to: applying a simultaneous localization and mapping algorithm to respectively calculate the transformation relation of adjacent point clouds in the first point cloud sequence; selecting a preset moment, and respectively converting the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the coordinate conversion relation of adjacent point clouds to form a new first point cloud sequence; combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment; applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence; transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence; and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map.
In an alternative way, the registration unit 602 is further configured to: traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm; optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm; traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm; and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
In an alternative way, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j Is a transformation relation of (a)，T j ＝F(P j-1 ,P j )。
In an alternative way, the registration unit 602 is further configured to: and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
In an alternative, the point cloud registration algorithm comprises an iterative closest point algorithm or a normal distribution transformation algorithm.
In an embodiment of the present invention, the external parameter calibration method includes: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, external parameter calibration of the laser radars is realized, and multiple laser radars with insufficient overlapping areas can be subjected to external parameter calibration by using a general point cloud registration algorithm.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the external parameter calibration method in any of the method embodiments.
The executable instructions may be particularly useful for causing a processor to:
at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar;
respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence;
and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result.
In an alternative, the first lidar and the second lidar, which are either moving or rotating, are located in a venue having a structured feature.
In one alternative, the executable instructions may be specifically operable to cause a processor to:
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the first point cloud sequence;
transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the coordinate transformation relation of the adjacent point clouds to form a new first point cloud sequence;
combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment;
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence;
transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence;
and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map.
In one alternative, the executable instructions may be specifically operable to cause a processor to:
Traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm;
optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence and the second point cloud sequence by using a universal graph optimization algorithm;
traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm;
and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
In an alternative way, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j -1,P j )。
In one alternative, the executable instructions may be specifically operable to cause a processor to:
and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
In an alternative, the point cloud registration algorithm comprises an iterative closest point algorithm or a normal distribution transformation algorithm.
In an embodiment of the present invention, the external parameter calibration method includes: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, external parameter calibration of the laser radars is realized, and multiple laser radars with insufficient overlapping areas can be subjected to external parameter calibration by using a general point cloud registration algorithm.
An embodiment of the present invention provides a computer program product comprising a computer program stored on a computer storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of calibrating the external parameters in any of the method embodiments described above.
The executable instructions may be particularly useful for causing a processor to:
at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar;
respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence;
and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result.
In an alternative, the first lidar and the second lidar, which are either moving or rotating, are located in a venue having a structured feature.
In one alternative, the executable instructions may be specifically operable to cause a processor to:
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the first point cloud sequence;
transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the coordinate transformation relation of the adjacent point clouds to form a new first point cloud sequence;
combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment;
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence;
transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence;
and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map.
In one alternative, the executable instructions may be specifically operable to cause a processor to:
Traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm;
optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm;
traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm;
and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
In an alternative way, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j -1,P j )。
In one alternative, the executable instructions may be specifically operable to cause a processor to:
and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
In an alternative, the point cloud registration algorithm comprises an iterative closest point algorithm or a normal distribution transformation algorithm.
In an embodiment of the present invention, the external parameter calibration method includes: at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, external parameter calibration of the laser radars is realized, and multiple laser radars with insufficient overlapping areas can be subjected to external parameter calibration by using a general point cloud registration algorithm.
Fig. 7 shows a schematic structural diagram of an embodiment of the apparatus according to the present invention, which is not limited to the specific implementation of the apparatus.
As shown in fig. 7, the apparatus may include: a processor 702, a communication interface (Communications Interface), a memory 706, and a communication bus 708.
Wherein: processor 702, communication interface 704, and memory 706 perform communication with each other via a communication bus 708. A communication interface 704 for communicating with network elements of other devices, such as clients or other servers. The processor 702 is configured to execute the program 710, and may specifically perform relevant steps in the above-described embodiment of the external parameter calibration method.
In particular, program 710 may include program code including computer-operating instructions.
The processor 702 may be a Central Processing Unit (CPU), or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors comprised by the device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 706 for storing programs 710. The memory 706 may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 710 may be specifically configured to cause the processor 702 to:
at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar;
respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence;
and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result.
In an alternative, the first lidar and the second lidar, which are either moving or rotating, are located in a venue having a structured feature.
In an alternative, the program 710 may be specifically configured to cause the processor 702 to:
Applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the first point cloud sequence;
transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the coordinate transformation relation of the adjacent point clouds to form a new first point cloud sequence;
combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment;
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence;
transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence;
and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment, wherein the second point cloud map is partially overlapped with the first point cloud map.
In an alternative, the program 710 may be specifically configured to cause the processor 702 to:
traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm;
Optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm;
traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm;
and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
In an alternative way, the new first point cloud sequence satisfies the following relation:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i For adjacent point clouds P in the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j-1 ,P j )。
In an alternative, the program 710 may be specifically configured to cause the processor 702 to:
and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
In an alternative, the point cloud registration algorithm comprises an iterative closest point algorithm or a normal distribution transformation algorithm.
In an embodiment of the present invention, the external parameter calibration method includes: at least collecting a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar; respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; and calculating a transformation relation from the second point cloud map to the first point cloud map by using a point cloud registration algorithm to obtain a calibration result. Therefore, the field of view of a single laser radar is enlarged through movement or rotation of a carrier loaded with the laser radar, point clouds of different coordinate systems at different moments are transformed into the same coordinate system, time registration of a plurality of laser radars is carried out, a point cloud map expressed under respective machine body coordinates at the same moment is used, the relative coordinate transformation relation among the point cloud maps is calculated, external parameter calibration of the laser radars is realized, and multiple laser radars with insufficient overlapping areas can be subjected to external parameter calibration by using a general point cloud registration algorithm.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (10)
1. An external parameter calibration method, which is characterized by comprising the following steps:
at least acquiring a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, wherein the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar;
Respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; the second point cloud map is partially overlapped with the first point cloud map;
calculating a transformation relation from the second point cloud map to the first point cloud map by applying a point cloud registration algorithm to obtain a calibration result;
the obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence respectively comprises transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moments to obtain a first point cloud map; and transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment to obtain a second point cloud map.
2. The method of calibrating a foreign object according to claim 1, wherein the first lidar and the second lidar, moving or rotating, are located in a field having a structured feature.
3. The method for calibrating the external parameters according to claim 1, wherein the step of obtaining a first point cloud map of the first lidar at a preset time according to the first point cloud sequence comprises:
Applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the first point cloud sequence;
transforming the first point cloud sequence to a body coordinate system of the first laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new first point cloud sequence;
combining the new first point cloud sequences to obtain the first point cloud map based on the body coordinate system of the first laser radar at the preset moment;
the obtaining the second point cloud map of the second laser radar at the preset moment according to the second point cloud sequence includes:
applying a simultaneous localization and mapping algorithm to calculate the transformation relationship of adjacent point clouds in the second point cloud sequence;
transforming the second point cloud sequence to a body coordinate system of the second laser radar at the preset moment according to the transformation relation of the adjacent point clouds to form a new second point cloud sequence;
and merging the new second point cloud sequences to obtain the second point cloud map based on the body coordinate system of the second laser radar at the preset moment.
4. The method of calibrating external parameters according to claim 3, wherein the calculating the transformation relation between adjacent point clouds in the first point cloud sequence by applying a simultaneous localization and mapping algorithm includes:
Traversing the first point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the first point cloud sequence by applying a point cloud registration algorithm;
optimizing the transformation relation of any adjacent point cloud in the first point cloud sequence by using a universal graph optimization algorithm;
the application of the simultaneous localization and mapping algorithm calculates a transformation relationship of adjacent point clouds in the second point cloud sequence, including:
traversing the second point cloud sequence, and calculating the transformation relation of any adjacent point cloud in the second point cloud sequence by applying a point cloud registration algorithm;
and optimizing the transformation relation of any adjacent point cloud in the second point cloud sequence by using a universal graph optimization algorithm.
5. A method of calibrating a foreign object as claimed in claim 3, wherein the new first point cloud sequence satisfies the following relationship:
P′ i ＝T 1 T 2 …T i P i ，
wherein P' i The coordinates of the ith point cloud in the new first point cloud sequence are that i is a positive integer and P i T is the coordinate of the ith point cloud in the first point cloud sequence i Adjacent point clouds P for the first point cloud sequence i-1 And P i T is the transformation relation of (1) i ＝F(P i-1 ,P i )；
The new second point cloud sequence satisfies the following relation:
P′ j ＝T 1 T 2 …T j P j ，
wherein P' j The coordinates of the j-th point cloud in the new second point cloud sequence are positive integers and P j T is the coordinate of the jth point cloud in the second point cloud sequence j For adjacent point clouds P in the second point cloud sequence j-1 And P j T is the transformation relation of (1) j ＝F(P j-1 ,P j )。
6. The method of calibrating a reference of claim 1, wherein the calculating a transformation relationship from the second point cloud map to the first point cloud map by applying a point cloud registration algorithm includes, before obtaining a calibration result:
and respectively carrying out filtering operation on the first point cloud map and the second point cloud map.
7. The method of any one of claims 1-6, wherein the point cloud registration algorithm comprises an iterative closest point algorithm or a normal distribution transformation algorithm.
8. An external reference calibration device, characterized in that the external reference calibration device comprises:
the system comprises a data acquisition unit, a first laser radar detection unit and a second laser radar detection unit, wherein the data acquisition unit is used for acquiring at least a first point cloud sequence of a moving or rotating first laser radar and a second point cloud sequence of a second laser radar, the first point cloud sequence is positioned in a body coordinate system of the first laser radar, and the second point cloud sequence is positioned in the body coordinate system of the second laser radar;
the registration unit is used for applying a simultaneous localization and mapping algorithm and respectively obtaining a first point cloud map of the first laser radar and a second point cloud map of the second laser radar at preset moments according to the first point cloud sequence and the second point cloud sequence; the second point cloud map is partially overlapped with the first point cloud map;
And the calibration unit is used for calculating the transformation relation from the second point cloud map to the first point cloud map by applying a point cloud registration algorithm to obtain a calibration result.
9. A computing device, the computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the steps of the method for calibrating a parameter according to any of claims 1-7.
10. A computer storage medium, characterized in that at least one executable instruction is stored in the storage medium, the executable instructions causing a processor to perform the steps of the external parameter calibration method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310416245.1A CN116577760A (en) | 2019-08-21 | 2019-08-21 | External parameter calibration device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2019/101813 WO2021031157A1 (en) | 2019-08-21 | 2019-08-21 | Extrinsic parameter calibration method and apparatus, computing device and computer storage medium |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310416245.1A Division CN116577760A (en) | 2019-08-21 | 2019-08-21 | External parameter calibration device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110741282A CN110741282A (en) | 2020-01-31 |
| CN110741282B true CN110741282B (en) | 2023-05-26 |
Family
ID=69274602
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201980002761.1A Active CN110741282B (en) | 2019-08-21 | 2019-08-21 | External parameter calibration method, device, computing equipment and computer storage medium |
| CN202310416245.1A Pending CN116577760A (en) | 2019-08-21 | 2019-08-21 | External parameter calibration device |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310416245.1A Pending CN116577760A (en) | 2019-08-21 | 2019-08-21 | External parameter calibration device |
Country Status (2)
| Country | Link |
|---|---|
| CN (2) | CN110741282B (en) |
| WO (1) | WO2021031157A1 (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114080547A (en) * | 2020-06-15 | 2022-02-22 | 深圳市大疆创新科技有限公司 | Calibration method and calibration device for multiple groups of laser radar external parameters and computer storage medium |
| CN114440922A (en) * | 2020-10-30 | 2022-05-06 | 阿里巴巴集团控股有限公司 | Method and device for evaluating laser calibration, related equipment and storage medium |
| CN112462350B (en) * | 2020-12-10 | 2023-04-04 | 苏州一径科技有限公司 | Radar calibration method and device, electronic equipment and storage medium |
| WO2022246826A1 (en) * | 2021-05-28 | 2022-12-01 | 深圳市大疆创新科技有限公司 | Extrinsic calibration method and apparatus, movable platform, and storage medium |
| CN113484843A (en) * | 2021-06-02 | 2021-10-08 | 福瑞泰克智能系统有限公司 | Method and device for determining external parameters between laser radar and integrated navigation |
| CN113465608B (en) * | 2021-07-22 | 2024-05-03 | 清华大学苏州汽车研究院（吴江） | Road side sensor calibration method and system |
| CN113702931A (en) * | 2021-08-19 | 2021-11-26 | 中汽创智科技有限公司 | External parameter calibration method and device for vehicle-mounted radar and storage medium |
| CN114152935B (en) * | 2021-11-19 | 2023-02-03 | 苏州一径科技有限公司 | Method, device and equipment for evaluating radar external parameter calibration precision |
| CN115235525B (en) * | 2021-12-07 | 2023-05-23 | 上海仙途智能科技有限公司 | Sensor detection method, sensor detection device, electronic equipment and readable storage medium |
| CN115100287A (en) * | 2022-04-14 | 2022-09-23 | 美的集团(上海)有限公司 | External reference calibration method and robot |
| CN114646932B (en) * | 2022-05-23 | 2022-10-21 | 深圳元戎启行科技有限公司 | Radar external parameter calibration method and device based on external radar and computer equipment |
| CN115236644A (en) * | 2022-07-26 | 2022-10-25 | 广州文远知行科技有限公司 | Laser radar external parameter calibration method, device, equipment and storage medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8743176B2 (en) * | 2009-05-20 | 2014-06-03 | Advanced Scientific Concepts, Inc. | 3-dimensional hybrid camera and production system |
| CN104793619B (en) * | 2015-04-17 | 2018-02-09 | 上海交通大学 | Based on the warehouse tunnel automatic guide vehicle guider for swinging single line laser radar |
| CN104764457B (en) * | 2015-04-21 | 2017-11-17 | 北京理工大学 | A kind of urban environment patterning process for unmanned vehicle |
| CN106872963B (en) * | 2017-03-31 | 2019-08-27 | 厦门大学 | A kind of automatic Calibration algorithm of multiple groups multi-line laser radar |
| CN108732584B (en) * | 2017-04-17 | 2020-06-30 | 百度在线网络技术（北京）有限公司 | Method and device for updating map |
| CN109839624A (en) * | 2017-11-27 | 2019-06-04 | 北京万集科技股份有限公司 | A kind of multilasered optical radar position calibration method and device |
| CN108648240B (en) * | 2018-05-11 | 2022-09-23 | 东南大学 | Non-overlapping view field camera attitude calibration method based on point cloud feature map registration |
-
2019
- 2019-08-21 CN CN201980002761.1A patent/CN110741282B/en active Active
- 2019-08-21 WO PCT/CN2019/101813 patent/WO2021031157A1/en active Application Filing
- 2019-08-21 CN CN202310416245.1A patent/CN116577760A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CN116577760A (en) | 2023-08-11 |
| WO2021031157A1 (en) | 2021-02-25 |
| CN110741282A (en) | 2020-01-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110741282B (en) | External parameter calibration method, device, computing equipment and computer storage medium | |
| US20220043449A1 (en) | Multi-channel sensor simulation for autonomous control systems | |
| WO2019127445A1 (en) | Three-dimensional mapping method, apparatus and system, cloud platform, electronic device, and computer program product | |
| CN111443359B (en) | Positioning method, device and equipment | |
| CN103424112B (en) | A kind of motion carrier vision navigation method auxiliary based on laser plane | |
| CN108326845B (en) | Robot positioning method, device and system based on binocular camera and laser radar | |
| CN103578117A (en) | Method for determining poses of camera relative to environment | |
| CN110378919B (en) | Narrow-road passing obstacle detection method based on SLAM | |
| CN110503713B (en) | Rotation axis estimation method based on combination of trajectory plane normal vector and circle center | |
| CN114485698B (en) | Intersection guide line generation method and system | |
| CN110673107A (en) | Road edge detection method and device based on multi-line laser radar | |
| CN106199601A (en) | InSAR absolute phase blur estimation method based on thick Digital height model | |
| CN114758063A (en) | Local obstacle grid map construction method and system based on octree structure | |
| CN113341401A (en) | Vehicle-mounted laser radar calibration method and device, vehicle and storage medium | |
| CN117590362B (en) | Multi-laser radar external parameter calibration method, device and equipment | |
| CN113256697B (en) | Three-dimensional reconstruction method, system, device and storage medium for underwater scene | |
| CN113670316A (en) | Path planning method and system based on double radars, storage medium and electronic equipment | |
| CN111765883B (en) | Robot Monte Carlo positioning method, equipment and storage medium | |
| CN115546216B (en) | Tray detection method, device, equipment and storage medium | |
| CN114494383B (en) | Light field depth estimation method based on Richard-Lucy iteration | |
| Su et al. | A simulation method for LIDAR of autonomous cars | |
| Oko et al. | Evaluation of image processing algorithms on vehicle safety system based on free-viewpoint image rendering | |
| WO2014033731A2 (en) | A system and method for depth estimation | |
| CN113513988A (en) | Laser radar target detection method and device, vehicle and storage medium | |
| Singandhupe et al. | Single frame lidar and stereo camera calibration using registration of 3d planes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |