CN113763549A - Method, device and storage medium for simultaneous positioning and mapping by fusing laser radar and IMU - Google Patents
Method, device and storage medium for simultaneous positioning and mapping by fusing laser radar and IMU Download PDFInfo
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Abstract
The invention relates to a method, a device and a storage medium for simultaneous positioning and mapping by fusing a laser radar and an IMU. After laser radar point cloud data and IMU data are preprocessed, a system is initialized, the preprocessed point cloud and an existing map are registered by adopting a direct method, relevant parameters are calculated so as to construct a factor map, an initial 3D sub-map is obtained, a 3D sub-map is mapped to a 2D sub-map, feature points are extracted, 2D rigid body transformation parameters are calculated, a 3D coordinate initial value is calculated, the value of a 3D coordinate is continuously updated through iterative optimization, and finally the 3D coordinate and the 3D map which meet the accuracy requirement are output. Compared with the prior art, the method adopts a direct method to register the point cloud with the existing map, and obtains final output through subsequent continuous iteration, and has the advantages of adaptability to multiple radar systems, high detection efficiency and the like.
Description
Technical Field
The invention relates to the field of simultaneous positioning and mapping, in particular to a method, a device and a storage medium for simultaneous positioning and mapping by fusing a laser radar and an IMU.
Background
Currently, in the fields related to autonomy, such as autonomous mapping and robotics, simultaneous localization and mapping (SLAM) is a very important basic technology. SLAM constructs an environmental map while providing real-time location estimation for the carrier, enabling higher level applications such as autonomous navigation, path planning, and the like to be implemented. However, in complex environments, such as tall buildings, trees, etc., where obstructions often make GPS, etc., unusable, one relies primarily on active sensors such as laser radar (LiDAR) and IMU for SLAM system construction. However, the existing fusion mode of the laser radar and the IMU directly uses the measurement value of the IMU for calculation, the real-time performance is lacked, the precision is low, and in the registration method of point cloud information, the characteristic point-based method is highly coupled with the point acquisition mode of the laser radar sensor, and the method is not easy to be expanded to a multi-radar system. Therefore, the problem of accuracy and adaptability still exists in the simultaneous positioning mapping method at present.
Disclosure of Invention
The present invention aims to overcome the defects of the prior art, and provides a method, an apparatus and a storage medium for simultaneous positioning and mapping by fusing a laser radar and an IMU, so as to solve the problems of low accuracy and poor adaptability of the current positioning and mapping method.
The purpose of the invention can be realized by the following technical scheme:
a simultaneous positioning and mapping method fusing a laser radar and an IMU comprises the following steps:
and S1, acquiring the point cloud data and the IMU data, and preprocessing the point cloud data and the IMU data.
S2, initializing a positioning and mapping system by using the preprocessed point cloud data and IMU data, registering the preprocessed point cloud data with the existing map according to the initialized positioning and mapping system to obtain an initial registration value, and calculating the current gravity vector, the initial attitude and an IMU integral value.
And S3, constructing a factor graph model according to the gravity vector, the registration value and the IMU pre-integration.
And S4, inputting the preprocessed point cloud data into the factor graph model to obtain real-time 3D carrier coordinates, and generating an initial 3D sub-map according to the 3D carrier coordinates and the preprocessed point cloud data.
And S5, projecting the initial 3D sub-map in the horizontal direction according to the initial posture to obtain a test 2D sub-map.
And S6, extracting the feature points of the test 2D sub-map, matching the feature points of the test 2D sub-map with the feature points of the existing 2D sub-map through a positioning map building system, and calculating 2D rigid body transformation parameters according to the matched feature points when the number of the matched feature points exceeds a number threshold.
S7, calculating to obtain a 3D coordinate initial value according to the preprocessed point cloud data, the initial 3D sub-map and the 2D rigid body transformation parameters, performing sliding registration in the Z direction by taking the 3D coordinate initial value as a starting point to obtain a progressive registration value, and calculating 3D coordinate conversion parameters according to the progressive registration value; and establishing a pose graph according to the 3D coordinate conversion parameters, and obtaining a further attitude. (ii) a
S8, judging that the difference of the two adjacent 3D coordinate conversion parameters is smaller than a parameter threshold value, if so, executing a step S10; if not, step S9 is executed.
And S9, keeping the original 3D sub-map unchanged, remapping the original 3D sub-map according to the advanced attitude to obtain a new 2D sub-map, and executing the step S6.
And S10, taking the 3D coordinates corresponding to the current 3D coordinate conversion parameters as final 3D coordinates, importing the final 3D coordinates into the initial 3D sub-map, and generating the final 3D sub-map.
Further, the initialization in step S2 includes static initialization or dynamic initialization.
Further, the static initialization includes: and calculating a rotation matrix between the average value of the IMU acceleration measurement values and the gravity vector of the world coordinate system, determining an initial attitude, setting the average value of the IMU angular velocity measurement values as an IMU deviation initial value, and setting the IMU velocity and the coordinate as 0.
Further, the dynamic initialization includes: establishing a starting frame coordinate system, and calculating a point cloud relative rotation value and a pre-integral relative rotation value to obtain an initial IMU deviation value; and calculating the point cloud relative displacement value and the pre-integral relative displacement value to obtain the IMU speed, the coordinates, the initial posture and the initial gravity vector, and converting all values into a world coordinate system according to the initial gravity vector.
Further, the feature points of the 2D sub-map tested in step S6 are SURF feature points.
Further, the preprocessing step in step S1 includes: pre-integrating IMUs with timestamps smaller than the time stamp of the main radar, and reordering all laser points with sampling timestamps in the auxiliary radar falling in the time stamp by taking the time stamp of the main radar as reference according to the time stamp; and converting each laser point into an IMU coordinate system, calculating relative motion and performing motion distortion correction to obtain preprocessed point cloud data.
Further, the number threshold in step S6 is in the range of 4 to 6.
Further, the point cloud data and the IMU data are acquired by a plurality of sixteen-line lidar and IMU in step S1.
A simultaneous positioning and mapping device fusing a laser radar and an IMU (inertial measurement Unit) comprises a memory and a processor; a memory for storing a computer program; a processor for, when executing a computer program, implementing the method of simultaneous localization mapping fusing lidar and an IMU as above.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method of simultaneous localization mapping fusing lidar and an IMU as described above.
Compared with the prior art, the invention has the following advantages:
1. the method for directly registering the preprocessed point cloud and the existing map is adopted, so that the system adaptability is stronger, parameter change can be reflected in real time, the system is easy to expand to a multi-radar input system, the 3D sub-map is mapped to the 2D sub-map, then the characteristic points are extracted, the 2D rigid body transformation parameters are calculated, the detection efficiency is improved, the initial coordinates can be obtained, and finally the coordinates are optimized in a circulating iteration mode, so that the result is more accurate.
2. The static initialization or the dynamic initialization is supported during the system initialization, and the application range is wide; and a world coordinate system is introduced in the initialization, which can be matched with an application using the coordinate system.
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FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a schematic diagram of processing feature points of a 2D sub-map according to the present invention.
FIG. 3 is a schematic diagram illustrating the spatial relationship conversion from a 2D sub-map to a 3D sub-map.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The embodiment provides a method for simultaneously positioning and constructing a map by fusing a laser radar and an IMU, wherein the process is shown in fig. 1 and specifically expanded as follows:
s1, point cloud data and IMU data of the multi-radar system are obtained and preprocessed: and pre-integrating IMU readings with timestamps less than the time stamp of the main radar to obtain pre-integrated measurement values and initial estimation values of relative motion. And taking the time stamps of the laser points in all the auxiliary radars as reference, taking all the points of which the sampling time stamps of the laser points in all the auxiliary radars fall in the interval, and reordering the points according to the time stamps to obtain the point cloud which grows according to the time sequence. And (4) carrying out seat standard conversion on each radar point to an IMU coordinate system. And calculating relative motion of each radar point from the IMU integral value according to the timestamp of the radar point, and performing motion distortion correction on the radar point to obtain the point cloud data subjected to distortion correction under the main radar timestamp.
And step S2, initializing the positioning and mapping system by using the preprocessed point cloud data and IMU data, wherein the initialization process can be divided into static initialization and dynamic initialization. The static initialization specifically comprises the steps of aligning the average value of the acceleration measurement values of the IMU with the gravity vector of the world coordinate system, calculating a rotation matrix between the two vectors, determining the IMU posture at the initial moment, and taking the average value of the IMU angular velocity measurement values as the initial value of the IMU gyroscope Bias. The position and velocity of the IMU are initialized to 0. The dynamic initialization specifically comprises the steps of registering multi-frame point clouds in a window by using a Normal Distribution Transform algorithm, calculating relative motion between the point clouds, calculating an IMU pre-integration value between adjacent frames under an initial frame coordinate system in the window, and calculating the IMU pre-integration value between adjacent frames. And simultaneously solving the bias of the IMU gyroscope by the point cloud relative rotation value and the pre-integrated relative rotation value, and simultaneously solving the position, the speed, the attitude and the gravity vector of the IMU in the initial time window by the point cloud relative displacement and the pre-integrated relative displacement. And converting the state values of all frames in the window into a world coordinate system according to the solved gravity vector and the state. And registering the preprocessed point cloud data with the existing map according to the initialized positioning mapping system to obtain an initial registration value, and calculating the current gravity vector, the initial attitude and an IMU integral value by using data of at least 3 frames in the past.
And S3, constructing a factor graph model according to the gravity vector, the registration value and the IMU pre-integration, and updating parameters such as IMU deviation.
And step S4, inputting the preprocessed point cloud data into a factor graph model to obtain real-time 3D carrier coordinates, and generating an initial 3D sub-map according to the 3D carrier coordinates and the preprocessed point cloud data.
And step S5, projecting the initial 3D sub-map in the horizontal direction according to the initial posture to obtain a test 2D sub-map.
Step S6, extracting SURF feature points of the test 2D sub-map, calculating a feature descriptor, and matching the feature points of the test 2D sub-map with the feature points of the existing 2D sub-map, as shown in fig. 2, when the number of the matched feature points exceeds 5, determining that two sub-maps exist in a loop, and calculating 2D rigid body transformation parameters according to the feature points of the sub-maps existing in the loop.
Step S7, as shown in fig. 3, calculating to obtain a 3D coordinate initial value according to a spatial relationship between an original frame and an initial 3D sub-map and a 2D rigid body transformation parameter, performing sliding registration in the Z direction with the 3D coordinate initial value as a starting point to obtain an advanced registration value, and calculating a 3D coordinate conversion parameter according to the advanced registration value by local iterative optimization; and establishing a pose graph according to the 3D coordinate conversion parameters, and performing combined optimization to obtain an advanced posture.
Step S8, judging that the difference of the two adjacent 3D coordinate conversion parameters is smaller than a parameter threshold value, if so, executing step S10; if not, step S9 is executed.
And step S9, keeping the original 3D sub-map unchanged, remapping the original 3D sub-map according to the advanced pose to obtain a new 2D sub-map, and executing step S6 (updating the values of the 3D conversion coordinates and the advanced pose).
And step S10, taking the 3D coordinates corresponding to the current 3D coordinate conversion parameters as final 3D coordinates, importing the final 3D coordinates into the initial 3D sub-map, and generating the final 3D sub-map.
The embodiment also provides a simultaneous positioning and mapping device fusing the laser radar and the IMU, which comprises a memory and a processor; a memory for storing a computer program; a processor for, when executing a computer program, implementing the method of simultaneous localization mapping fusing lidar and an IMU as described above.
The present embodiment also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of a method for simultaneous localization and mapping in a fusion lidar and an IMU as described above.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A simultaneous positioning and mapping method fusing a laser radar and an IMU is characterized by comprising the following steps:
s1, point cloud data and IMU data are obtained, and the point cloud data and the IMU data are preprocessed;
s2, initializing a positioning and mapping system by using the preprocessed point cloud data and IMU data, registering the preprocessed point cloud data with the existing map according to the initialized positioning and mapping system to obtain an initial registration value, and calculating a current gravity vector, an initial posture and an IMU integral value;
s3, constructing a factor graph model according to the gravity vector, the registration value and the IMU pre-integration;
s4, inputting the preprocessed point cloud data into the factor graph model to obtain real-time 3D carrier coordinates, and generating an initial 3D sub-map according to the 3D carrier coordinates and the preprocessed point cloud data;
s5, projecting the initial 3D sub-map in the horizontal direction according to the initial posture to obtain a test 2D sub-map;
s6, extracting feature points of the test 2D sub-map, matching the feature points of the test 2D sub-map with the feature points of the existing 2D sub-map through a positioning map building system, and calculating 2D rigid body transformation parameters according to the matched feature points when the number of the matched feature points exceeds a number threshold;
s7, calculating to obtain a 3D coordinate initial value according to the preprocessed point cloud data, the initial 3D sub-map and the 2D rigid body transformation parameters, performing sliding registration in the Z direction by taking the 3D coordinate initial value as a starting point to obtain a progressive registration value, and calculating 3D coordinate conversion parameters according to the progressive registration value; establishing a pose graph according to the 3D coordinate conversion parameters, and obtaining a progressive gesture;
s8, judging that the difference of the two adjacent 3D coordinate conversion parameters is smaller than a parameter threshold value, if so, executing a step S10; if not, go to step S9;
s9, keeping the original 3D sub-map unchanged, re-mapping to obtain a new 2D sub-map according to the advanced attitude, and executing the step S6;
and S10, taking the 3D coordinates corresponding to the current 3D coordinate conversion parameters as final 3D coordinates, importing the final 3D coordinates into the initial 3D sub-map, and generating the final 3D sub-map.
2. The method of claim 1, wherein the initialization in step S2 comprises static initialization or dynamic initialization.
3. The method of claim 2, wherein the static initialization comprises: and calculating a rotation matrix between the average value of the IMU acceleration measurement values and the gravity vector of the world coordinate system, determining an initial attitude, setting the average value of the IMU angular velocity measurement values as an IMU deviation initial value, and setting the IMU velocity and the coordinate as 0.
4. The method of claim 2, wherein the dynamic initialization comprises: establishing a starting frame coordinate system, and calculating a point cloud relative rotation value and a pre-integral relative rotation value to obtain an initial IMU deviation value; and calculating the point cloud relative displacement value and the pre-integral relative displacement value to obtain the IMU speed, the coordinates, the initial posture and the initial gravity vector, and converting all values into a world coordinate system according to the initial gravity vector.
5. The method for simultaneous localization and mapping of a lidar and an IMU according to claim 1, wherein the feature points of the 2D sub-map tested in step S6 are SURF feature points.
6. The method for simultaneous localization and mapping of lidar and IMU according to claim 1, wherein the preprocessing step in step S1 comprises: pre-integrating IMUs with timestamps smaller than the time stamp of the main radar, and reordering all laser points with sampling timestamps in the auxiliary radar falling in the time stamp by taking the time stamp of the main radar as reference according to the time stamp; and converting each laser point into an IMU coordinate system, calculating relative motion and performing motion distortion correction to obtain preprocessed point cloud data.
7. The method for simultaneous localization and mapping of a lidar and an IMU according to claim 1, wherein the quantity threshold in step S6 is 4-6.
8. The method for simultaneous localization and mapping of a lidar and an IMU according to claim 1, wherein the point cloud data and IMU data are obtained by a plurality of sixteen-line lidar and IMU in step S1.
9. A simultaneous positioning and mapping device fusing a laser radar and an IMU (inertial measurement Unit) is characterized by comprising a memory and a processor; the memory for storing a computer program; the processor, when executing the computer program, is configured to implement the method for simultaneous localization and mapping of fusion lidar and an IMU according to any of claims 1 to 8.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of a method of simultaneous localization and mapping of a fusion lidar and an IMU according to any of claims 1 to 8.
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